Methods for displaying an e-book using a combination of colors for text and background that have a reduced myopiagenic effect

ABSTRACT

Methods for displaying an e-book using a combination of colors for text and background that have a reduced myopiagenic effect compared to black text on white background, include: presenting a user with one or more combinations of colors for the text and background identified as having a reduced myopiagenic effect, wherein none of the presented combinations comprise either black or white text or either black or white background, and, when viewed by the user&#39;s retina, an image composed of text and background rendered in any of the presented color combinations provides reduced center-surround contrast on the user&#39;s retina compared to the image viewed as black text on white background; receiving a selection of one of the color combinations from the user; and displaying the e-book file using the combination of colors for the text and background selected by the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of Provisional Application No.62/279,954, entitled “EVALUATING AND REDUCING MYOPIAGENIC EFFECTS OFELECTRONIC DISPLAYS,” filed on Jan. 18, 2016. The entire contents ofthis priority application is hereby incorporated by reference.

BACKGROUND

Electronic displays are ubiquitous in today's world. For example, mobiledevices such as smartphones and tablet computers commonly use a liquidcrystal display (LCD) or an organic light emitting diode (OLED) display.LCDs and OLED displays are both examples of flat panel displays, and arealso used in desktop monitors, TVs, and automotive and aircraftdisplays.

Many color displays, including many LCD and OLED displays, spatiallysynthesize color. In other words, each pixel is composed of threesub-pixels that provide a different color. For instance, each pixel canhave a red, green, or blue sub-pixel, or a cyan, magenta, or yellowsub-pixel. The color of the pixel, as perceived by a viewer, dependsupon the relative proportion of light from each of the three sub-pixels.

Color information for a display is commonly encoded as an RGB signal,whereby the signal is composed of a value for each of the red, green,and blue components of a pixel color for each signal in each frame. Aso-called gamma correction is used to convert the signal into anintensity or voltage to correct for inherent non-linearity in a display,such that the intended color is reproduced by the display.

In the field of color science when applied to information display,colors are often specified by their chromaticity, which is an objectivespecification of a color regardless of its luminance. Chromaticityconsists of two independent parameters, often specified as hue (h) andsaturation (s). Color spaces (e.g., the 1931 CIE XYZ color space or theCIELUV color space) are commonly used to quantify chromaticity. Forinstance, when expressed as a coordinate in a color space, a pixel's hueis the angular component of the coordinate relative to the display'swhite point, and its saturation is the radial component. Once colorcoordinates are specified in one color space, it is possible totransform them into other color spaces.

Humans perceive color in response to signals from photoreceptor cellscalled cone cells, or simply cones. Cones are present throughout thecentral and peripheral retina, being most densely packed in the foveacentralis, a 0.3 mm diameter rod-free area in the central macula. Movingaway from the fovea centralis, cones reduce in number towards theperiphery of the retina. There are about six to seven million cones in ahuman eye.

Humans normally have three types of cones, each having a response curvepeaking at a different wavelength in the visible light spectrum. FIG. 1Ashows the response curves for each cone type. Here, the horizontal axisshows light wavelength (in nm) and the vertical scale shows theresponsivity. In this plot, the curves have been scaled so that the areaunder each cone is equal, and adds to 10 on a linear scale. The firsttype of cone responds the most to light of long wavelengths, peaking atabout 560 nm, and is designated L for long. The spectral response curvefor L cones is shown as curve A. The second type responds the most tolight of medium-wavelength, peaking at 530 nm, and is abbreviated M formedium. This response curve is curve B in FIG. 1A. The third typeresponds the most to short-wavelength light, peaking at 420 nm, and isdesignated S for short, shown as curve C. The three types have typicalpeak wavelengths near 564-580 nm, 534-545 nm, and 420-440 nm,respectively; the peak and absorption spectrum varies among individuals.The difference in the signals received from the three cone types allowsthe brain to perceive a continuous range of colors, through the opponentprocess of color vision.

In general, the relative number of each cone type can vary. WhereasS-cones usually represent between 5-7% of total cones, the ratio of Land M cones can vary widely among individuals, from as low as 5% L/95% Mto as high as 95% L/5% M. The ratio of L and M cones also can vary, onaverage, between members of difference races, with Asians believed toaverage close to 50/50 L:M and Caucasians believed to average close to63% L cones (see, for example, U.S. Pat. No. 8,951,729). Color visiondisorders also impact the proportion of L and M cones; protanopes have0% L cones and deuteranopes have 0% M cones. Referring to FIG. 1B, conesare generally arranged in a mosaic on the retina. In this example, L andM cones are distributed in approximately equal numbers, with fewer Scones. Accordingly, when viewing an image on an electronic display, theresponse of the human eye to a particular pixel will depend on the colorof that pixel and where on the retina the pixel is imaged.

SUMMARY

It is known in the art that exposure to outdoor sunlight is not a riskfactor for myopia (see, for example Jones, L. A. et al. Invest.Ophthalmol. Vis. Sci. 48, 3524-3532 (2007)). Sunlight is considered anequal energy (EE) illuminant because it does not trigger the opponentcolor visual system (i.e., sunlight is neither red nor green, andneither blue nor yellow). The EE illuminant represents a ‘white point’in the CIE 1931 color space diagram, which is shown in FIG. 1C. Asopposed to visual exposure to EE illumination like sunlight, it wasrecently described that excessive stimulation of L cones relative to Mcones can lead to asymmetric growth in a developing human eye, leadingto myopia (see, for example, patent application WO 2012/145672 A1). Thishas significant implications for electronic displays, which areconventionally optimized to display images with deeply saturated colors,including reds, and high contrast. It is believed that the myopiageniceffect of displays may be reduced by reducing the saturation of red-huedpixels in an image, or reducing the relative amount of red to green in apixel's color, particularly in those pixels where the amount of redexceeds the amount of green.

A more recent discovery stipulates that overall contrast betweenneighboring cones stimulates asymmetric growth of the eye, leading tomyopia. This could be, for example, excessive stimulation of L conesover M cones, but is not limited to that type of contrast alone. Thediscovery further stipulates that difference in stimulation inneighboring cones is critical, as opposed to the overall ratio of L vs.M over the entire retina.

When a high contrast image falls upon the retina, edges in the image aredetected in the visual system by center-surround antagonism in areceptive field on the retina. Thus images with many edges can be saidto contain high contrast, causing signaling differences between adjacentneurons in the retina (cone photoreceptors and their downstreamsignaling partners, including bipolar cells and retinal ganglion cells),which highly activate center-surround antagonism in the visual system.Similarly, when an image containing saturated red, which is composedprimarily of long wavelength light, falls upon the retina, it stronglystimulates L cones but not M cones or S cones. Each L cone, wheresurrounded by a number of M cones and/or S cones, acts as a highlystimulated “center” whereas the M or S cones in the “surround” arestimulated to a much lesser degree. In this way, saturated red colorscan be said to provide high contrast among adjacent retinal neurons andcan be said to activate a high degree of center-surround antagonism.Because high contrast causes high signaling differences between adjacentcones and other neurons in the visual system, and cause highcenter-surround antagonism in the visual system, these terms are usedinterchangeably to describe the degree of contrast within a receptivefield on the retina.

The instant invention builds upon both recent biological discoveries todescribe new methods, algorithms, and devices that can determine thelevel of myopiagenicity and reduce it, relative to current methodsfamiliar to skilled artisans. Accordingly, among other aspects, thepresent disclosure features ways to characterize and/or reducemyopiagenic effects of displays while minimizing the viewer's perceptionof the correction on the image, and characterize and/or reduce contrastbetween neighboring cones in the retina.

In general, the myopiagenic reduced techniques described may beimplemented in a variety of ways. For example, the techniques may beimplemented in TV sets via a stand-alone set top box, or via hardware(e.g., as an image processing chip) and/or software integration with theTV set itself, the cable box, or other product that interfaces with a TVset. In addition to TV sets, the techniques may be implemented incomputer monitors, mobile devices, automobile display, aviationdisplays, wearable displays, and other applications using colordisplays.

In some embodiments, the color scheme of content can be modified beforedelivery to an end user so that the end user gets the benefit of themyopiagenia reduction without the use of any additional hardware orsoftware. For example, myopiagenia reduced content can be delivered tothe end user via the internet or from a cable provider.

Techniques for quantifying the myopiagenic effect of a stimulus are alsodisclosed. Such techniques allow for comparison of different myopiagenicreducing algorithms on a stimulus. Implementations also account for bothchromatic (e.g., how much red is in an image) and spatial (e.g., howmuch high-contrast high spatial frequency content there exists in animage) contributions of a stimulus to myopiagenia. Implementations allowfor this being calculated and described either as the amount of contrastbetween adjacent neurons in the retina or the degree of center-surroundantagonism in a receptive field.

Various aspects of the invention are summarized below.

In general, in a first aspect, the invention features a method,including: receiving initial image data for a sequence of framesincluding a first frame, f₁ ^(i), and a second frame, f₂ ^(i), whereindata for each pixel in f₁ ^(i) and f₂ ^(i) include a value, r^(i), for afirst color, a value, g^(i), for a second color, and a value, b^(i), fora third color; for at least one pixel in f₁ ^(i), determining a relativelevel of stimulation of cones in a viewer's eye based, at least, on thevalue, r^(i), for the first color and the value, g^(i), for the secondcolor; generating modified image data for the sequence of framesincluding a second frame, f₂ ^(m) corresponding to the second frame, f₂^(i), of the initial image data, where f₂ ^(m) includes a value, r^(m),for the first color and a value, g^(m), for the second color for thefirst pixel based on the level of stimulation of cones in a viewer's eyeby the at least one pixel in f₁ ^(i); and transmitting the modifiedimage data to an electronic display. While the term “frame” often refersto a frame in a video file, it is intended to encompass images fromnon-video files as well. For example, a frame can include any changingor stationary image produced by a display, such as a page in a webbrowser, a page in an e-reader, a screen rendering in a video game, etc.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects.

Determining a relative level of stimulation of cones can includedetermining a relative level of stimulation of neighboring cones in theviewer's eye.

When viewed on the electronic display, f₂ ^(m) may results in reducedcontrast between neighboring cones in a viewer's eye compared to f₂^(i).

The second frame can occur after the first frame in the sequence.

In some embodiments, determining the relative level of stimulationincludes comparing the value, r^(i), for the first color to the value,g^(i), for the second color. r^(i) can be compared to g^(i) for aplurality of pixels in the first frame of the initial image data. Insome implementations, for the first pixel, r^(m)/g^(m)<r^(i)/g^(i) wheng^(i)≦r^(i). r^(m)/g^(m) can be equal to r^(i)/g^(i) when g^(i)>r^(i).When g^(i)≦r^(i), r^(m)/g^(m) can be equal to a·r^(i)/g^(i), where 0<a<1and the value of a can depend on a number of frames in the sequencepreceding f₂ ^(i). a can increase as the number of frames in thesequence preceding f₂ ^(i) increases.

f₂ ^(m) can include at least one pixel for which r^(m)=r^(i) andg^(m)=g^(i). For the pixel in f₂ ^(m) for which r^(m)=r^(i) andg^(m)=g^(i), g^(i) can be greater than r^(i).

In certain embodiments, b^(m)≠b^(i) for at least one pixel in f₂ ^(m).

Determining the relative level of stimulation can include determiningcoordinates in a universal chromaticity space representative of thecolor of the first pixel. The chromaticity space is the 1931 x, y CIEchromaticity space or the CIE XYZ chromaticity space, or the 1964 or1976 CIE chromaticity space.

The relative level of stimulation can be based on a relative spectralsensitivity of L-cones and M-cones in the viewer's eye. The relativelevel of stimulation can be further based on a relative spectralsensitivity of S-cones in the viewer's eye. The relative level ofstimulation can be further based on a relative proportion of L-cones toM-cones in the viewer's eye. The relative level of stimulation can befurther based on a pixel/cone ratio of the frame when viewed.

The first, second, and third colors can be red, green, and blue,respectively. In some cases, the first, second, and third colors arecyan, magenta, and yellow.

The relative level of stimulation can be determined based on L, M, and Svalues determined based on at least some of the pixel's in f₁ ^(i).

In general in another aspect, the invention features an apparatus thatincludes: an electronic processing module including an electronicprocessor, an input (e.g., electrical contacts such as electrodes forhardwiring or standard electrical connectors), and an output (e.g.,electrical contacts such as electrodes for hardwiring or standardelectrical connectors), wherein: the input is configured to receiveinitial image data for a sequence of frames including a first frame, f₁^(i), and a second frame, f₂ ^(i), wherein data for each pixel in f₁^(i) and f₂ ^(i) includes a value, r^(i), for a first color, a value,g^(i), for a second color, and a value, b^(i), for a third color; theelectronic processor is programmed to receive the initial image datafrom the input and, for at least one pixel in f₁ ^(i), configured tocompare the value, r^(i), for the first color to the value, g^(i), forthe second color and to generate modified image data for the sequence offrames including a second frame, f₂ ^(m) corresponding to the secondframe, f₂ ^(i), of the initial image data, where f₂ ^(m) includes avalue, r^(m), for the first color and a value, g^(m), for the secondcolor for the first pixel based on a relative level of stimulation ofcones in a viewer's eye for the at least one pixel in f₁ ^(i); and theoutput is configured to transmit the modified image data from theelectronic processing module.

Embodiments of the apparatus can include one or more of the followingfeatures and/or features of other aspects. The electronic processor canbe programmed to generate modified image data based on a relative levelof stimulation of neighboring cones in the viewer's eye.

The electronic processing module can be programmed to determine therelative level of stimulation based, at least, on the correspondingvalues of r^(i) and g^(i) and b^(i) for the at least one pixel in f₁^(i).

The apparatus can include an electronic display panel configured toreceive the modified image data from the output and display the sequenceof frames based on the modified image data. The electronic display canbe a display selected from the group including a liquid crystal display,a digital micromirror display, an organic light emitting diode display,a projection display, quantum dot display, and a cathode ray tubedisplay.

In some embodiments, the apparatus is a semiconductor chip or a circuitboard including a semiconductor chip.

In other aspects, the invention features a set top box, a flat paneldisplay, a television, a mobile device, a wearable computer, aprojection display, and/or a video game console including the foregoingapparatus.

The set top box can be configured to receive the input from another settop box, a DVD player, a video game console, or an internet connection.

In general, in another aspect, the invention features a method,including: assessing uncorrected image data corresponding to a sequenceof frames by identifying pixels having a red hue in each of the sequenceof frames; providing modified image data corresponding to the sequenceof frames based on the uncorrected image data and the assessment;displaying the sequence of frames including at least one corrected framebased on the modified image data, where one or more red-hued pixels inthe corrected frame has a reduced degree of red saturation compared tothe corresponding pixel in the uncorrected frame, wherein the degree ofred saturation in the one or more red-hued pixels in the corrected frameis reduced based on the degree of red saturation in red-hued pixels inone or more of the frames displayed prior to displaying the correctedframe.

Implementations of the method can include one or more features of otheraspects.

In general, in a further aspect, the invention features an apparatusthat includes an input configured to receive uncorrected image datacorresponding to a sequence of frames; an electronic processing moduleincluding an electronic processor, an input, and an output, the inputbeing configured to receive uncorrected image data corresponding to asequence of frames, the electronic processor being programmed to assessthe uncorrected image data by identifying pixels having a red hue ineach of the sequence of frames and configured to provide modified imagedata corresponding to the sequence of frames based on the uncorrectedimage data and the assessment, and the output being configured totransmit the modified image data from the electronic processing moduleto an electronic display. The modified image data corresponds to thesequence of frames including at least one corrected frame, where one ormore red-hued pixels in the corrected frame has a reduced degree of redsaturation compared to the corresponding pixel in the uncorrected frame,the degree of red saturation in the one or more red-hued pixels in thecorrected frame being reduced based on the degree of red saturation inred-hued pixels in one or more of the frames preceding the correctedframe.

Embodiments of the apparatus can include one or more features of otheraspects.

In general, in another aspect, the invention features a method,including: receiving initial image data including a first frame, f₁^(i), wherein data for each pixel in f₁ ^(i) includes a value, r^(i),for a first color, a value, g^(i), for a second color, and a value,b^(i), for a third color; for at least a first pixel f₁ ^(i), comparingthe value, r^(i), for the first color to the value, g^(i), for thesecond color; generating modified image data including a first frame, f₁^(m), including a value, r^(m), for the first color at a second pixeland a value, g^(m), for the second color at the second pixel, the secondpixel being at a different location in the first frame from the firstpixel, wherein a ratio r^(m)/g^(m) for the second pixel is differentfrom a ratio r^(i)/g^(i) for the second pixel, the difference betweenthe ratios being based on r^(i) and g^(i) of the first pixel in f₁ ^(i);and transmitting the modified image data to an electronic display.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects.

Determining a relative level of stimulation of cones can includedetermining a relative level of stimulation of neighboring cones in theviewer's eye.

When viewed on a display, f₁ ^(m) can stimulates L cones in a viewer'seye less relative to M cones in the viewer's eye than f₁ ^(i).

The difference between the ratios can also be based on r^(i) and g^(i)of the second pixel in f₁ ^(i). The difference between the ratios can bebased on r^(i) and g^(i) of one or more additional pixels in f₁ ^(i)different from the first and second pixels.

The first pixel can be an n-th nearest neighbor to the second pixel. Forexample, the first pixel can be a nearest neighbor pixel to the secondpixel.

For the second pixel, r^(m)/g^(m) can be less than r^(i)/g^(i) wheng^(i)≦r^(i).

For the second pixel, r^(m)/g^(m) can be equal to r^(i)/g^(i) wheng^(i)>r^(i).

For the second pixel, when g^(i)≦r^(i), r^(m)/g^(m) can be equal toa·r^(i)/g^(i), where 0<a<1 and the value of a can depend on a r^(i) andg^(i) of the first pixel. a can decrease as a ratio r^(i)/g^(i) for thefirst pixel increases.

r^(m) can be less than r^(i) for the second pixel. g^(m) can be greaterthan g^(i) for the second pixel.

b^(m) can be non-equal to b^(i) for at least some of the pixels.

The first, second, and third colors can be red, green, and blue,respectively. In some embodiments, the first, second, and third colorsare cyan, magenta, and yellow.

In general, in another aspect, the invention features an apparatus,including: an input configured to receive initial image data including afirst frame, f₁ ^(i), wherein data for each pixel in f₁ ^(i) includes avalue, r^(i), for a first color, a value, g^(i), for a second color, anda value, b^(i), for a third color; an electronic processing moduleprogrammed to receive the initial image data from the input and, for atleast a first pixel f₁ ^(i), compare the value, r^(i), for the firstcolor to the value, g^(i), for the second color and to generate modifiedimage data including a first frame, f₁ ^(m), including a value, r^(m),for the first color at a second pixel and a value, g^(m), for the secondcolor at the second pixel, the second pixel being at a differentlocation in the first frame from the first pixel, wherein a ratior^(m)/g^(m) for the second pixel is different from a ratio r^(i)/g^(i)for the second pixel, the difference between the ratios being based onr^(i) and g^(i) of the first pixel in f₁ ^(i); and an output configuredto transmit the modified image data from the electronic processingmodule.

Embodiments of the apparatus can include one or more of the followingfeatures and/or features of other aspects.

In general, in another aspect, the invention features a method,including: assessing uncorrected image data corresponding to at leastone uncorrected frame by identifying pixels having a red hue in the atleast one uncorrected frame; providing modified image data based on theuncorrected image data and the assessment, the modified image datacorresponding to at least one corrected frame corresponding to the atleast one uncorrected frame; displaying the at least one correctedframe, where one or more red-hued pixels in the corrected frame has areduced degree of red saturation compared to the corresponding pixel inthe uncorrected frame, wherein the degree of red saturation in the oneor more red-hued pixels in the corrected frame is reduced based on acomparison of a degree of red saturation in two or more differentportions of the uncorrected frame.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects. For example, the two or moredifferent portions can be red-hued portions. The different portions caninclude one or more contiguous pixels.

The uncorrected image data can correspond to a plurality of uncorrectedframes and the modified image data includes a corresponding plurality ofcorrected frames.

In general, in a further aspect, the invention features an apparatus,including: an electronic processing module including an electronicprocessor, an input, and an output, wherein: the input is configured toreceive uncorrected image data corresponding to at least one uncorrectedframe; the electronic processor is programmed to assess the uncorrectedimage data by identifying pixels having a red hue in the at least oneuncorrected frame and to provide modified image data based on theuncorrected image data and the assessment; and the output is configuredto transmit the modified image data from the electronic processingmodule to an electronic display, wherein the modified image datacorresponds to at least one corrected frame, where one or more red-huedpixels in the corrected frame has a reduced degree of red saturationcompared to the corresponding pixel in the uncorrected frame, andwherein the degree of red saturation in the one or more red-hued pixelsin the corrected frame is reduced based on a comparison of a degree ofred saturation in two or more different portions of the uncorrectedframe.

Embodiments of the apparatus can include one or more of the followingfeatures and/or features of other aspects. For example, the apparatuscan include an electronic display panel configured to receive themodified image data from the output and display the sequence of framesbased on the modified image data. The electronic display can be adisplay selected from the group including a liquid crystal display, adigital micromirror display, an organic light emitting diode display, aprojection display, quantum dot display, and a cathode ray tube display.

In some embodiments, the apparatus is a semiconductor chip or a circuitboard including a semiconductor chip.

In other aspects, the invention features a set top box, a flat paneldisplay, a television, a mobile device, a wearable computer, aprojection display, and/or a video game console including the foregoingapparatus.

The set top box can be configured to receive the input from another settop box, a DVD player, a video game console, or an internet connection.

In general, in a further aspect, the invention features a method,including: receiving initial image data including a first frame, f₁^(i), wherein data for each pixel in the first frame includes a value,r^(i), for a first color, a value, g^(i), for a second color, and avalue, b^(i), for a third color; for at least a first pixel in f₁ ^(i),comparing r^(i) to g^(i); generating modified image data including amodified first frame, f₁ ^(m), the modified first frame including avalue, r^(m), for the first color and a value, g^(m), for the secondcolor at the first pixel, wherein r^(m) is different from r^(i) for thefirst pixel and/or g^(m) is different from g^(i) for the first pixel,the difference being based on a location of the first pixel in the firstframe; and transmitting the modified image data to an electronicdisplay.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects. For example, the differencebetween r^(m) and r^(i) can increase the closer the location of thefirst pixel is to a nearest border of the display.

The difference between g^(m) and g^(i) can decrease the closer thelocation of the first pixel is to a nearest border of the display. Thedifference between r^(m) and r^(i) can increase the closer the locationof the first pixel is to a center of the display. The difference betweeng^(m) and g^(i) can decrease the closer the location of the first pixelis to a center of the display.

In some embodiments, b^(m)≠b^(i) for at least one pixel.

In general, in a further aspect, the invention features an apparatus,including: an electronic processing module including an electronicprocessor, an input, and an output, wherein: the input is configured toreceive initial image data for a sequence of frames including a firstframe, f₁ ^(i), wherein data for each pixel in f₁ ^(i) includes a value,r^(i), for a first color, a value, g^(i), for a second color, and avalue, b^(i), for a third color; the electronic processor is programmedto receive the initial image data from the input and, for at least onepixel in f₁ ^(i), configured to compare r^(i) to g^(i) and to generatemodified image data including a modified first frame, f₁ ^(m), themodified first frame including a value, r^(m), for the first color and avalue, g^(m), for the second color at the first pixel, wherein r^(m) isdifferent from r^(i) for the first pixel and/or g^(m) is different fromg^(i) for the first pixel, the difference being based on a location ofthe first pixel in the first frame; and the output configured totransmit the modified image data from the electronic processing module.

Embodiments of the apparatus can include one or more features of otheraspects.

In general, in a further aspect, the invention features a method,including: assessing uncorrected image data corresponding to at leastone uncorrected frame by identifying pixels having a red hue in the atleast one uncorrected frame; providing modified image data based on theuncorrected image data and the assessment, the modified image datacorresponding to at least one corrected frame corresponding to the atleast one uncorrected frame; displaying the at least one correctedframe, where one or more red-hued pixels in the corrected frame has areduced degree of red saturation compared to the corresponding pixel inthe uncorrected frame, wherein the degree of red saturation in the oneor more red-hued pixels in the corrected image frame is reduced based ona respective location of the one or more pixels in the corrected frame.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects. For example, the degree ofred saturation in the one or more red-hued pixels in the corrected imageframe can be reduced based on a proximity of the red-hued pixels to anedge of the corrected frame. The degree of red saturation can be reducedmore for pixels closer to the edge of the corrected frame than forpixels further from the edge of the corrected frame.

In general, in another aspect, the invention features an apparatus,including: an electronic processing module including an electronicprocessor, an input, and an output, wherein: the input is configured toreceive uncorrected image data corresponding to at least one uncorrectedframe; the electronic processor is programmed to assess the uncorrectedimage data by identifying pixels having a red hue in the at least oneuncorrected frame and to provide modified image data based on theuncorrected image data and the assessment; and the output is configuredto transmit the modified image data from the electronic processingmodule to an electronic display, wherein the degree of red saturation inthe one or more red-hued pixels in the corrected image frame is reducedbased on a respective location of the one or more pixels in thecorrected frame.

Embodiments of the apparatus can include one or more features of otheraspects.

In general, in a further aspect, the invention features a method,including: receiving initial image data including a first frame, f₁^(i), wherein data for each pixel in the first frame includes a valuefor a first color, r^(i), a value for a second color, g^(i), and a valuefor a third color, b^(i); for at least a first pixel in f₁ ^(i),calculating a degree of stimulation by the first pixel on a first set ofone or more cones in a viewer's eye based, at least, on r^(i) and g^(i)and b^(i) for the first pixel; for at least a second pixel in f₁ ^(i),different from the first pixel, calculating a degree of stimulation bythe second pixel on a second set of one or more cones in the viewer'seye based, at least, on r^(i) and g^(i) and b^(i) for the second pixel;determining a difference in a degree of stimulation between the firstand second sets of one or more cones by the first and second pixels; andgenerating modified image data including a modified first frame, f₁^(m), the modified first frame including a value for the first color,r^(m), a value for the second color, g^(m), and a value for the thirdcolor, b^(m), at the first pixel, wherein one or more of r^(m) g^(m)and/or b^(m) is modified compared to r^(i), g^(i), and/or b^(i),respectively, and the difference in the degree of stimulation betweenthe first and second sets of one or more cones by the first pixel isreduced for the modified image data compared to the initial image data;and transmitting the modified image data to an electronic display.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects. For example, the cones of thefirst set can be from one cone type (L, M or S) and the cones of thesecond set are a different cone type (L, M, or S).

The first and second pixels can be neighboring pixels or groups ofpixels. The at least one second pixel can include each of the pixelsneighboring the first pixel.

Calculating the degree of stimulation can include determiningcorresponding coordinates in a universal chromaticity spacerepresentative of the colors of the first and second pixels. Thechromaticity space can be the 1931 x, y CIE chromaticity space or theCIE XYZ chromaticity space, or the 1964 or 1976 CIE chromaticity space.

The degree of stimulation can be based on the relative spectralsensitivity of L-cones and M-cones in the viewer's eye. The degree ofstimulation can be further based on a relative proportion of L-cones toM-cones in the viewer's eye. The degree of stimulation can be furtherbased on a pixel/cone ratio of the image when viewed.

A red saturation of the first pixel can be reduced in the modified imagedata relative to the initial image data.

A contrast between the first pixel and the second pixel can be reducedin the modified image data relative to the initial image data.

r^(i) can be greater than r^(m) and/or g^(i) can be less than g^(m). Insome embodiments, b^(i)≠b^(m) for at least one pixel.

In general, in another aspect, the invention features an apparatus,including: an electronic processing module including an electronicprocessor, an input, and an output, wherein: the input is configured toreceive initial image data for a sequence of frames including a firstframe, f₁ ^(i), wherein data for each pixel in f₁ ^(i) includes a valuefor a first color, r^(i), a value for a second color, g^(i), and a valuefor a third color, b^(i); the electronic processor is programmed to: (i)receive the initial image data from the input, for at least one pixel inf₁ ^(i); (ii) to calculate a degree of stimulation by the first pixel ona first set of one or more cones in a viewer's eye based, at least, onr^(i) and g^(i) for the first pixel; (iii) for at least a second pixelin f₁ ^(i), different from the first pixel, calculate a degree ofstimulation by the second pixel on a second set of one or more cones inthe viewer's eye based, at least, on r^(i) and g^(i) for the secondpixel; (iv) determine a difference in a degree of stimulation betweenthe first and second sets of one or more cones by the first and secondpixels; and generate modified image data including a modified firstframe, f₁ ^(m), the modified first frame including a value for the firstcolor, r^(m), a value for the second color, g^(m), and a value for thethird color, b^(m), at the first pixel, wherein the difference in thedegree of stimulation between the first and second sets of one or morecones by the first pixel is reduced for the modified image data comparedto the initial image data; and the output is configured to transmit themodified image data from the electronic processing module.

Embodiments of the apparatus can include one or more of the followingfeatures and/or features of other aspects. For example, the cones of thefirst set are L-cones and the cones of the second set are M-cones.

The first and second pixels can be neighboring pixels. The at least onesecond pixel can include each of the pixels neighboring the first pixel.

The electronic processing module can be programmed to determine therelative level of stimulation based, at least, on the correspondingvalues of r^(i) and g^(i) for the at least one pixel in f₁ ^(i).

The apparatus can include an electronic display panel configured toreceive the modified image data from the output port and display thesequence of frames based on the modified image data. The electronicdisplay is a display selected from the group including a liquid crystaldisplay, a digital micromirror display, an organic light emitting diodedisplay, a projection display, and a cathode ray tube display.

In some embodiments, the apparatus is a semiconductor chip or a circuitboard including a semiconductor chip.

In other aspects, the invention features a set top box, a flat paneldisplay, a television, a mobile device, a wearable computer, aprojection display, and/or a video game console including the foregoingapparatus.

The set top box can be configured to receive the input from another settop box, a DVD player, a video game console, or an internet connection.

In general, in another aspect, the invention features a method ofevaluating differential stimulation between neighboring sets of cones ofa viewer's eye when viewing an image on an electronic display, themethod including: calculating a degree of stimulation of a pixel in theimage on a first set of one or more cones based, at least, on a color ofthe pixel; calculating a degree of stimulation of a pixel in the imageon a second set of one or more cones based, at least, on a color of thesecond pixel; and determining a difference in the degree of stimulationbetween the first and second sets of one or more cones.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects. For example, the cones of thefirst set can be L-cones and the cones of the second set can be M-cones.

The first and second pixels can be neighboring pixels. The at least onesecond pixel can include each of the pixels neighboring the first pixel.

Calculating the degree of stimulation can inclide determiningcorresponding coordinates in a two-dimensional chromaticity spacerepresentative of the colors of the first and second pixels. Thechromaticity space can be the 1931 x, y CIE chromaticity space or theCIE XYZ chromaticity space, or the 1964 or 1976 CIE chromaticity space.The degree of stimulation can be based on the relative spectralsensitivity of L-cones and M-cones in the viewer's eye. The degree ofstimulation can be further based on a relative proportion of L-cones toM-cones in the viewer's eye. The degree of stimulation can be furtherbased on a pixel/cone ratio of the image when viewed.

The method can include evaluating a myopiagenic effect of a digitalvideo file including the image based on the difference in the degree ofstimulation between the first and second sets of one or more cones. Thedigital video file can include a sequence of frames, and at least one ofthe frames includes the image.

The method can include assigning the digital video file a scoreindicative of the myopiagenic effect of the digital video file based onthe evaluation.

The method can include modifying the color of the first pixel and/or thesecond pixel to reduce the difference in the degree of stimulationbetween the first and second sets of one or more cones. The colormodification can reduce a red saturation of the first pixel and/or thesecond pixel. Alternatively, or additionally, the color modification canreduce a contrast between the first pixel and the second pixel.

In general, in another aspect, the invention features a method forevaluating a myopiagenic effect of a digital video file, including:determining, for at least a first pixel in a first frame of the digitalvideo file, a relative level of stimulation of L-cones and a level ofstimulation of M-cones in a viewer's eye by the first pixel based on acolor of the first pixel; and assigning a score to the digital videofile indicative of the myopiagenic effect of the digital video filebased on the relative level of L-cone and M-cone stimulation by thefirst pixel in the first frame.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects. For example, determining therelative level of stimulation of the L-cones and M-cones can includetranslating color data for each pixel to a co-ordinate in atwo-dimensional chromaticity space. The chromaticity space can be the1931 x, y CIE chromaticity space or the CIE XYZ chromaticity space, orthe 1964 or 1976 CIE chromaticity space.

A value for the relative level of stimulation of the L-cones and M-conescan be assigned to each pixel based on the coordinate for that pixel.

The method can include determining a level of stimulation of L-cones anda level of stimulation of M-cones in the viewer's eye by one or moreadditional pixels in the first frame based on a color of each of therespective additional pixels; and assigning the score based on acontrast between the relative levels of M-cone and L-cone stimulationbetween the first pixel and the additional pixels. The one or moreadditional pixels can neighbor the first pixel in the frame. There canbe six or eight additional pixels.

Determining the relative level of stimulation of the L-cones and M-conescan include translating color data for each pixel to a coordinate in atwo-dimensional chromaticity space and assigning each pixel a value forthe relative level of stimulation of the L-cones and M-cones based onthe coordinate for that pixel.

Assigning the score includes calculating a neighbor sum of squares (NSS)based on the value for the relative level of stimulation. NSS can becalculated for multiple pixels in the first frame. The score can beassigned based on an average of the NSS of the multiple pixels in thefirst frame. Assigning the score can include accounting for a relativedensity of L-cones to M-cones in the viewer's eye. Assigning the scorecan include accounting for a pixel/cone ratio of the frame when viewed.

The determining can be repeated for multiple frames in the digital videofile and the score can be assigned based on the determination for eachof the multiple frames.

The method can include normalizing the score indicative of themyopiagenic effect of the digital video file and outputting thenormalized score.

The method can include assigning the digital video file an alphanumericgrade based on the score indicative of the myopiagenic effect of thedigital video file and outputting the alphanumeric grade.

The method can include displaying the alphanumeric grade with a mediumcontaining the digital video file or a link to the digital video file.

The digital video file can have a format selected from the groupconsisting of MPEG, MP4, MOV, WMV, FLV, AVI, AVC, AVCHD, Divx, and MXF.

In general, in a further aspect, the invention features a method,including: assessing image data corresponding to pixels from one or moreframes by identifying pixels having a red hue in at least one of theframes and determining a degree of red saturation for each of thered-hued pixels; and assigning a score to the image data based on theassessment, the score corresponding to a degree to which the image data,when viewed on an electronic display, differentially stimulates L-conesto M-cones in a viewer's eye.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects. For example, the data foreach pixel in the image data can include a value, r, for a first color,a value, g, for a second color, and a value, b, for a third color thepixels having a red hue are identified by comparing r, g, and b for eachpixel. The first color can be red, the second color can be green, andthe third color can be blue.

Red-hued pixels can be identified as pixels for which r>g and r>b.

The first color can be cyan, the second color can be magenta, and thethird color can be yellow.

The score can be an alphanumeric score. The method can includedisplaying the score in association with the image data.

The image data can be stored on a storage medium and the score isdisplayed on the medium or packaging for the medium.

The image data can be provided via the internet and the score isdisplayed in association with a hyperlink to the image data.

The image data can be formatted as a digital video file.

In general, in another aspect, the invention features a method,including: accessing an electronic file including text; displaying atleast one letter of text on at least one area of background in amodified format on a color LCD display; wherein the average variance oraverage absolute difference in L/M cone stimulation is reduced by morethan 60% compared to the unmodified format in the displayed area.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects. For example, the electronicfile can be an e-book. The electronic file can be a text file forreading or word processing.

The area of modified format can be chosen according to the area beingread at that moment in time (e.g., based on eye-tracking or a touchsensor). Alternatively, or additionally, the area of modified format canbe chosen according to the area not being read at that moment in time.

Mathematically, the scale can be based on a measure of difference orvariance, for example. For a measure of difference, one can calculatetext stimulation on L cones, text stimulation on M cones, backgroundstimulation on L cones, and background stimulation on M cones. For eachsmall area of the retina, calculate the average stimulation overall.Then calculate the absolute value of the difference for each cone versusthe average for that area. Divide this result by the averagestimulation, and average this value over the entire simulated retina.

For a measure of variance, one can calculate text stimulation on Lcones, text stimulation on M cones, background stimulation on L cones,and background stimulation on M cones. For each small area of theretina, calculate the average stimulation overall. Then calculate thedifference for each cone and square it. Divide this result by theaverage stimulation, and average this value over the entire simulatedretina.

In general, in another aspect the invention features a method,including: receiving an electronic file including a text, optionally ona mobile device, including a display; selecting a display mode fordisplaying the text from the group consisting of a color display modeand a contrast display mode; and displaying a page of the text on theflat panel display using the selected display mode, wherein: for thecolor display mode, the text is displayed in a text color and abackground is displayed in a background color, wherein the text andbackground colors have at least a 30% myopia reduction compared to blacktext on a white background based on the LMS Myopia Scale, and for thecontrast display mode, a first area of the page of text is displayedwith a first contrast level between the text and the background and asecond area of the page of text is displayed with a second contrastlevel lower than the first level.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects. For example, the text andbackground colors can have at least a 35%, 40%, 45%, 50%, 55%, 60%, or65% myopia reduction (e.g., 68% or more, 70% or more, 75% or more, 80%or more, 85% or more, 90% or more, such as about 95%) compared to blacktext on a white background based on the LMS Myopia Scale.

The method can include presenting a user with a selection ofcombinations of colors for the text and background colors, and allowingthe user to select one of the combinations for the myopia-safe contrastdisplay scheme.

The second contrast level can be provided by changing a luminance levelof the background and/or the text. Alternatively, or additionally, thesecond contrast level can be provided by blurring edges of the text inthe displayed page.

Displaying the page of text can include scanning the first area over thepage of text. The first area can be determined based on the words thatare being viewed.

The mobile device can include a camera facing the viewer, and the mobiledevice can track the movement of the viewer's eyes using the camera todetermine which words are being viewed.

The first area can be scanned at a speed corresponding to 100 to 500words of the text per minute.

The display mode can be selected by accessing the electronic file usinga mobile app on the mobile device.

The electronic file can be an e-book file. The mobile device can be asmart phone, tablet computer, or dedicated e-reader. More generally, thedevice can be a personal computer (e.g., desktop or laptop) or otherdevice that includes a monitor.

In general, in another aspect, the invention features a mobile device,including: a display; an electronic processing module in communicationwith the display, the electronic processing module being programmed to:receive an electronic file including a text; receive a selection of adisplay mode for displaying the text, the display mode being selectedfrom the group consisting of a color display mode and a contrast displaymode; and display, on the display a page of the text using the selecteddisplay mode, wherein: for the color display mode, the text is displayedin a text color and a background is displayed in a background color,wherein the text and background colors have at least a 30%, 35%, 40%,45%, 50%, 55%, or 60% myopia reduction compared to black text on a whitebackground based on the LMS Myopia Scale, and for the contrast displaymode, a first area of the page of text is displayed with a firstcontrast level between the text and the background and a second area ofthe page of text is displayed with a second contrast level lower thanthe first level.

Embodiments of the mobile device can include one or more features ofother aspects.

In general, in a further aspect, the invention features a non-transitorycomputer-readable medium storing a program causing a mobile device toperform steps including: receiving an electronic file including a texton the mobile device; selecting a display mode for displaying the textfrom the group consisting of a color display mode and a myopia-safecontrast display mode; and displaying a page of the text on a flat paneldisplay of the mobile device using the selected display mode, wherein:for the color display mode, the text is displayed in a text color and abackground is displayed in a background color, wherein the text andbackground colors have at least a 60% myopia reduction compared to blacktext on a white background based on the LMS Myopia Scale, and for thecontrast display mode, a first area of the page of text is displayedwith a first contrast level between the text and the background and asecond area of the page of text is displayed with a second contrastlevel lower than the first level.

In general, in yet a further aspect, the invention features a method fordisplaying an e-book using a combination of colors for text andbackground that have a reduced myopiagenic effect compared to black texton white background, the method including: presenting a user with one ormore combinations of colors for the text and background identified ashaving a reduced myopiagenic effect, wherein none of the presentedcombinations include either black or white text or either black or whitebackground, and, when viewed by the user's retina, an image composed oftext and background rendered in any of the presented color combinationsprovides reduced center-surround contrast on the user's retina comparedto the image viewed as black text on white background; receiving aselection of one of the color combinations from the user; and displayingthe e-book file using the combination of colors for the text andbackground selected by the user.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects. For example, the reducedcenter-surround contrast due to the color combinations yields amyopiagenic effect reduced by at least 35% (e.g., 40% or more, 50% ormore, 60% or more, 80% or more, such as up to 90%) as calculated using amyopia scale that calculates a center-surround contrast of a modeledvisual receptive field and assigns a score to the color combinationsbased on the calculated center-surround contrast. The center-surroundcontrast can be calculated based on a difference between an averagestimulation of the visual receptive field center versus its and astimulation of the surround. The visual receptive field center cancorrespond to a cone and the surround to its nearest neighbors. Theaverage stimulation can be determined based on LMS stimulus values ofthe cone and its nearest neighbors of the visual receptive field.

The method can further include receiving information about a desiredmyopiagenic level from the user and presenting the one or morecombinations of colors according to the received information, thepresented combinations of colors having a myopiagenic effectcorresponding to the desired level. The information about the desiredmyopiagenic level can be a desired percentage reduction of myopiapotential as calculated using a myopia scale that calculates an impacton the retina based on a differential stimulation between the center andsurround of a modeled visual receptive field. The presented combinationsof colors can have a myopiagenic level within 10% (e.g., within 5%, 3%,2%, 1%) of the desired percentage reduction of myopia potential ascalculated using the myopia scale. The myopia scale can be a LMS MyopiaScale.

The e-book can be a file in any of the following formats: BroadbandeBooks (BBeB), Comic Book Archive, Compiled HTML, DAISY, DjVu, DOC,DOCX, EPUB, eReader, FictionBook, Founder Electronics, HTML, iBook,IEC62448, INF, KF8, KPF, Microsoft LIT, MOBI, Mobipocket, MultimediaeBooks, Newton eBook, Open Electronic Package, PDF, Plain text, Plucker,PostScript, RTF, SSReader, Text Encoding Initiative, TomeRaider, andOpen XML Paper Specification.

The e-book can be displayed on a mobile device, such as a smartphone, atablet computer, or a dedicated e-reader (e.g., a Kindle e-reader, aNook e-reader).

In general, in a further aspect, the invention features a device fordisplaying an e-book, including: a display; an interface for receivinginput from a user; and an electronic processing module programmed tocause the device to: (i) present the user with one or more combinationsof colors for text and background identified as having a reducedmyopiagenic effect, wherein none of the presented combinations includeeither black or white text or either black or white background, and,when viewed by the user's retina, an image composed of text andbackground rendered in any of the presented color combinations providesreduced center-surround contrast on the user's retina compared to theimage viewed as black text on white background; (ii) receive a selectionof one of the color combinations from the user via the interface; (iii)retrieve the e-book from memory; and (iv) display, using the display,the e-book using the combination of colors for the text and backgroundselected by the user.

Embodiments of the device can include one or more of the followingfeatures and/or features of other aspects. For example, the reducedcenter-surround contrast due to the color combinations can yield amyopiagenic effect reduced by at least 35% (e.g., 40% or more, 50% ormore, 60% or more, 70% or more, 80% or more, up to 90%) as calculatedusing a myopia scale that calculates a center-surround contrast of amodeled visual receptive field and assigns a score to the colorcombinations based on the calculated center-surround contrast. Thecenter-surround contrast can be calculated based on a difference betweenan average stimulation of the visual receptive field and a stimulationof the surround. The visual receptive field can correspond to a cone andits nearest neighbors.

The electronic processing module can be further programmed to cause thedevice to receive information about a desired myopiagenic level from theuser and present the one or more combinations of colors according to thereceived information, the presented combinations of colors having amyopiagenic effect corresponding to the desired level. The informationabout the desired myopiagenic level can be a desired percentagereduction of myopia potential as calculated using a myopia scale thatcalculates an impact on the retina based on a differential stimulationbetween the center and surround of a modeled visual receptive field.

The interface can include a touch panel, mouse, or keyboard.

The display can be a flat panel display.

The device can be a smartphone, a tablet computer, or a dedicatede-reader.

In general, in another aspect, the invention features a method fordisplaying an e-book using a combination of colors for text andbackground that have a reduced myopiagenic effect compared to black texton white background, the method including: displaying text using a textcolor other than black or white; and displaying a background to the textusing a background color other than black or white, wherein an imagedisplayed using the displayed text color on the displayed backgroundcolor, when viewed by the user's retina, provides reducedcenter-surround contrast on the user's retina compared to the image whenviewed in black and white.

Implementations of the method can include one or more of the followingfeatures and/or features of other aspects. The text color and backgroundcolor can yield a ratio of a Text Readability score to myopia score on aLMS myopia scale is greater than 0.60 (e.g., 0.65 or more, 0.7 or more,0.75 or more).

The myopia potential can be reduced by more than 58% as calculated usinga LMS myopia scale and a Text Readability score is decreased no morethan 65% (e.g., 60% or less, 50% or less, 40% or less) compared to theimage when viewed as black text on white background. Among otheradvantages, the disclosed implementations can reduce the myopiageniceffect of electronic displays.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a plot showing normalized responsivity spectra of human conecells, S, M, and L types.

FIG. 1B shows an example of cone mosaic on a retina.

FIG. 1C is CIE 1931 chromaticity diagram showing equal energy illuminantpoints CIE-E, CIE-D65, and CIE-C.

FIG. 2 shows an embodiment of a system including a set top box forreducing the myopiagenic effect of a TV set.

FIG. 3 shows another embodiment of a system including a set top box forreducing the myopiagenic effect of a TV set.

FIG. 4A shows an embodiment of a local area network including a serverfor delivering content for which the myopiagenic effect has beenreduced.

FIGS. 4B-4C show side cross-sections of a myopic eye and a normal eye,respectively.

FIG. 5A shows a stimulus composed of a black and white checkerboardarray.

FIG. 5B shows a distribution of L, M, and S cones in a simulated retina.

FIG. 5C shows a level of stimulation of the cones in the simulatedretina shown in FIG. 5B by the stimulus shown in FIG. 5A.

FIG. 6A shows a stimulus composed of an array of red pixels.

FIG. 6B shows a distribution of L, M, and S cones in a simulated retina.

FIG. 6C shows a level of stimulation of the cones in the simulatedretina shown in FIG. 6B by the stimulus shown in FIG. 6A.

FIG. 7 shows a flowchart of an algorithm for producing a modified videosignal for reducing the myopiagenic effect of a display.

FIG. 8A shows a stimulus for which the watercolor effect has been usedto reduce the myopiagenic effect of the image.

FIG. 8B shows a stimulus for which the cornsweet effect has been used toreduce the myopiagenic effect of the image.

FIG. 9 is a flowchart showing an algorithm for determining a conestimulation level in a simulated retina.

FIG. 10 is a flowchart showing an algorithm for quantifying themyopiagenic effect of a stimulus.

FIGS. 11A and 11B show possible arrangements of cones in a simulatedretina.

FIG. 12A is a schematic diagram showing the relationship between viewingdistance and cone separation at maximal retinal resolution.

FIG. 12B is a schematic diagram illustrating a cone to pixel mapping fora 1080P 60″ display.

FIG. 13 is a three-dimensional plot of calculated myopiagenic scalevalues as a function of different text and background colors.

FIG. 14A is a table listing calculated myopiagenic scale values andreadability values for different text and background color combinations.

FIG. 14B is another table listing calculated myopiagenic scale valuesand readability values for different text and background colorcombinations.

FIG. 15A is a further table listing calculated myopiagenic scale valuesand readability values for two text and background color combinations.

FIG. 15B is a plot showing calculated cone stimulation from a strip oftext between two strips of background for the color combinationspecified in the first row of the table in FIG. 15A.

FIG. 15C is a plot showing calculated cone stimulation from a strip oftext between two strips of background for the color combinationspecified in the second row of the table in FIG. 15A.

FIG. 16A is another table listing calculated myopiagenic scale valuesand readability values for two additional text and background colorcombinations.

FIG. 16B is a plot showing calculated cone stimulation from a strip oftext between two strips of background for the color combinationspecified in the first row of the table in FIG. 16A.

FIG. 16C is a plot showing calculated cone stimulation from a strip oftext between two strips of background for the color combinationspecified in the second row of the table in FIG. 16A.

FIG. 17 is a flowchart showing an algorithm for displaying an e-bookwith a combination of colors for text and background that have a reducedmyopiagenic effect compared to black text on white background;

FIG. 18. is a schematic diagram of an electronic processing module.

Like reference numbers and designations in various drawings indicatelike elements.

DETAILED DESCRIPTION

Referring to FIG. 2, a set top box 100 for reducing the myopiageniceffect of a television (TV) set 130 is connected between a cable box 120and TV set 130. A cable 125 connects an output port of cable box 120 toan input port of set top box 100, and another cable 135 connects anoutput port of set top box 100 to an input port of TV set 130. Cables125 and 135 are cables capable of carrying a video signal, includinganalogue video cables (e.g., composite video cables, S-video cables,component video cables, SCART cables, VGA cables) and digital videocables (e.g., serial digital interface (SDI) cables, digital visualinterface (DVI) cables, HDMI cables, DisplayPort cables).

Set top box 100 includes an electronic processing module 110 and aninternal power supply 140. Electronic processing module 110 includes oneor more electronic processors programmed to receive an input videosignal from the input port of set top box 100 and output a modifiedvideo signal to the output port. In general, a variety of electronicprocessors can be used, such as an application-specific integratedcircuit (ASIC) or a general purpose integrated circuit (e.g., a fieldprogrammable gate array or FPGA) programmed appropriately. Electronicprocessing module 110 may include other integrated circuit components(e.g., one or more memory blocks) and/or electronic components.

Internal power supply 140 is connected to a power port, to which a powersupply cable 105 is connected. The power supply cable 105 connects settop box 100 to an external power source, such as a standard plug socket.Power supply 140 is configured to receive electrical power from theexternal power source and convert that power to power appropriate forpowering electronic processing module 110 (e.g., AC-to-DC conversion atsuitable current and voltage levels). Internal wiring connects powersupply 140 to electronic processing module 110.

TV set 130 may include any appropriate color display including, forexample, a light emitting diode display (LEDs), liquid crystal displays(LCD), a LED-backlit LCD, an organic light emitting diode (OLED)display, a color projector displays, a quantum dot display, a cathoderay tube (CRT), or a MEMS-based display, such as a digital micromirrordevice (DMD). TV set 130 may be a direct view display or a projectiondisplay (e.g., a front or rear projection display).

During operation, cable box 120 receives an input signal, including avideo signal, from a source via cable 122. In general, cable 122 can beany of a variety of cables capable of carrying a video signal, such asan Ethernet cable, a co-axial cable, a DSL line. The input signal sourcecan be a satellite dish, a cable TV and/or broadband internet provider,or a VHF or UHF antenna. Furthermore, the input signal can includecontent in addition to video signals, such as audio signals, internetweb pages, interactive video games, etc.

Cable box 120 directs an input RGB video signal to set top box 100 viacable 125. The input video signal includes a sequence of image frames.Each frame is composed of a series of rows and columns of pixels,possibly arranged as a pixel array, and the input video signal includesinformation about the color of each pixel in each frame. In general, theinput RGB video signal includes, for each pixel in each frame, a valuefor red, r^(i), and value for green, g^(i), and a value for blue, b^(i).Typically, the higher the value for each color, the higher the intensityof the primary contributing to the pixel color. The range of values foreach color depends on the number of bits, or color depth, of the signal.For 24-bit color, for example, each component color has a value in arange from 0 to 255, yielding 256³ possible color combinations. Othercolor depths 8-bit color, 12-bit color, 30-bit color, 36-bit color, and48-bit color.

More generally, alternative forms for color coding in video signals toRGB may be used (e.g., Y′CbCr, Y′UV) and algorithms for transforming RGBsignals to other color signal formats and back are known.

The electronic processing module 110 generates an output RGB videosignal based on the input video signal so that the corresponding imagedisplayed using TV 130 produces either (i) a reduced level ofdifferential stimulation between L cones and M cones in a viewer's eyeand/or (ii) a reduced level of differential stimulation betweenneighboring cones, compared with the viewing an image produced using theinput video signal. The electronic processing modules achieves this byoutputting a video signal that includes, for each pixel in each frame,having a value for red, r^(m), a value for green, g^(m), and a value forblue, b^(m), based on at least the respective values r^(i), g^(i), andb^(i) for the corresponding pixel in the corresponding frame in theinput video signal. In order to provide reduced myopiagenia in thedisplayed image, for certain pixels either r^(m)≠r^(i), g^(m)≠g^(i),and/or b^(m)≠b^(i). In general, the video signal modification can varydepending on the factors that include, e.g., settings on TV 130, contentbeing viewed, viewing time, viewer's retinal composition, viewer's age,viewer's race or ethnicity, viewer's color vision status, etc. Exemplaryalgorithms for video signal modification are described below.

While set top box 100 includes an internal power supply 140, otherconfigurations are also possible. For example, in some embodiments, anexternal power supply is used. Alternatively, or additionally, set topbox 100 can draw power from batteries or from cable box 120 via cable125 or a separate cable connecting the two components. Set top box 100can include additional components, such as memory buffers for bufferinginput signals before processing them, or modified signals afterprocessing them before sending them to TV set 130. Memory buffers mayreduce latency during operation.

Moreover, while the components depicted in FIG. 2 are connected to eachother via physical cables, in general, one or more of the connectionscan be wireless connections (e.g., Wi-Fi connections or Bluetooth).

Referring to FIG. 3, in some embodiments, the electronic processingmodule for reducing the myopiagenic effect is housed in the TV setitself, rather than as a separate set top box as previously described.Here, a TV set 200 includes an electronic processing module 210 inaddition to a display panel 230 and display driver 220. A cable 205connects cable box 120 to TV set 200.

Electronic processing module 210 operates in a similar way as electronicprocessing module 110 described above in that it receives an input videosignal from cable box 120 and outputs a modified video signal that forreduced myopiagenia. Electronic processing module 210 directs themodified video signal to display driver 220, which in turn directs drivesignals to display panel 230 to display the modified images.

Furthermore, while the foregoing examples described in FIGS. 2 and 3receive digital video signals from a cable box, the video signals can befrom other sources. For example, video signals may be supplied from avideo game console or television set top box instead of (or in additionto) a cable box. For instance, video signals from commercially-availableset top box (such as Roku, Apple TV, Amazon Fire, etc.) or digital videorecording (DVR) device such as TiVO or similar, video game consoles,such as X-box consoles (from Microsoft Corp., Redmond Wash.),PlayStation consoles (from Sony Corp., New York, N.Y.), or Wii consoles(from Nintendo, Redmond, Wash.), can be modified.

Other implementations are also possible. For example, referring to FIG.4, in some embodiments, a modified video signal is provided by anetworked server 320 via a WAN 310 (e.g., the internet) to one or moreend users 340-344 and no additional hardware is required by the enduser. The original (unmodified) video signal may be received bynetworked server 330 from either a networked provider 330 or viabroadcast signal (e.g., VHF, UHF, or satellite signal) from abroadcaster 350.

While the foregoing examples relate to modifying color in a TV set, theconcepts disclosed herein may be generally applied to other devices thatcontain a color display. For example, the concepts may be implemented incomputer monitors, digital signage displays, mobile devices (e.g., smartphones, tablet computers, e-readers), and/or wearable displays (e.g.,head-mounted displays such as virtual reality and augmented realityheadsets, Google glass, and smart watches).

Moreover, while the foregoing examples utilize a dedicated electronicprocessing module for modifying display signals, other implementationsare also possible. For example, in some embodiments, video signalmodification can be applied via software solutions alone. In otherwords, video signals can be modified using software solutions installedon existing hardware (e.g., using a display's video card or a computer'sor mobile device's processor).

In some embodiments, video signals are modified using an app downloaded,e.g., from the internet. For instance, on a mobile device (e.g., runningGoogle's Android operating system or Apple's iOS operating system)signal modification may be implemented using a downloaded app.

More generally, versions of the system can be implemented in software,in middlewear, in firmware, in digital electronic circuitry, or incomputer hardware, or in combinations of them. The system can include acomputer program product tangibly embodied in a machine-readable storagedevice for execution by a programmable processor, and method steps canbe performed by a programmable processor executing a program ofinstructions to perform functions by operating on input data andgenerating output. The system can be implemented in one or more computerprograms that are executable on a programmable system including at leastone programmable processor coupled to receive data and instructionsfrom, and to transmit data and instructions to, a data storage system,at least one input device, and at least one output device. Each computerprogram can be implemented in a high-level procedural or object-orientedprogramming language, or in assembly or machine language if desired; andin any case, the language can be a compiled or interpreted language.Suitable processors include, by way of example, both general and specialpurpose microprocessors. Generally, a processor will receiveinstructions and data from a read-only memory and/or a random accessmemory. Generally, a computer will include one or more mass storagedevices for storing data files; such devices include magnetic disks,such as internal hard disks and removable disks; magneto-optical disks;and optical disks. Storage devices suitable for tangibly embodyingcomputer program instructions and data include all forms of non-volatilememory, including by way of example semiconductor memory devices, suchas EPROM, EEPROM, and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM disks. Any of the foregoing can be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

The Myopiagenic Effect

Before discussing algorithms for modifying video signals, it isinstructive to consider the cause of the myopiagenic effect ofelectronic displays. Myopia—or nearsightedness—is a refractive effect ofthe eye in which light entering the eye produces image focus in front ofthe retina, as shown in FIG. 4B for a myopic eye, rather than on theretina itself, as shown in FIG. 4C for a normal eye. Without wishing tobe bound by theory, it is believed that television, reading, indoorlighting, video games, and computer monitors all cause progression ofmyopia, particularly in children, because those displays produce stimulithat cause uneven excitation of L and M cones (for example stimulating Lcones more than M cones) and/or uneven excitation of neighboring conesin the retina. During childhood (approximately age 8), adolescence(before age 18), and young adulthood (until age 25 years or age 30years), these factors of differential stimulation result in abnormalelongation of the eye, which consequently prevents images from befocused on the retina.

There are two factors in an image that can result in a high degree ofretinal cone contrast and high center-surround antagonism in the visualsystem: one spatial and one chromatic. The spatial factor refers to thedegree to which an image contains high spatial frequency, high contrastfeatures. Fine contrast or detail, such as black text on a white page,form a high contrast stimulation pattern on the retinal cone mosaic. Thechromatic factor refers to how uniform blocks of highly saturated colorsstimulate cone types asymmetrically, and therefore form a high contrastpattern on the retina. For example, red stimulates L cones more than Mcones, whereas green light stimulates M cones more than L cones. Shorterwavelength light, such as blue, stimulates S cones more than either L orM cones. The degree of color can refer to either the number of pixels ofthat color as well as their saturation levels, or both. Here, forexample, red pixels may be identified as pixels for which r is greaterthan g and/or b by a threshold amount or a percentage amount.Alternatively, or additionally, red pixels may be identified as pixelsthat have a red hue in the 1931 or 1976 CIE color space. Similarly,green pixels could be identified as pixels for which g is greater than rand/or b by a threshold or percentage amount; or green pixels may beidentified as pixels that have a green hue in the 1931 or 1976 CIE colorspace. Similarly, blue pixels could be identified as pixels for which bis greater than r or g by a threshold amount or a percentage amount; orblue pixels could be identified as pixels that have a blue hue in the1931 and 1976 CIE color space.

Referring to FIGS. 5A-5C and 6A-6C, the spatial and chromatic effectscan be explained as follows. Each figure shows a hexagonal mosaic,corresponding to the spatial mosaic of cones on a retina. Thearrangement of cones is depicted in FIGS. 5B and 6B, where the L conesare colored red, the M cones are colored green, and the S cones arecolored blue. FIGS. 5A and 6A show two different types of stimuli at theretina and FIGS. 5C and 6C depict the cone responses due to therespective stimuli.

The stimuli in FIG. 5A corresponds to a high frequency, high contrastcheckerboard pattern of white and black across the retina. As usedherein, black refers to a pixel in its darkest state and white refers toa pixel in its brightest state. For 8-bit color in an RGB color system,for example, black is typically represented by the values (0, 0, 0) andwhite by (255, 255, 255). The spatial frequency of the checkerboardpattern is half the spatial frequency of the cones so, on a row by rowbasis, every alternate cone is has a high response (due to stimulationby white light) and the adjacent cones see no response (because there isno incident light at all). This response is depicted in FIG. 6C and theresult is a high degree of differential stimulation in the cone mosaic,including between at least some of the L cones and some of the M cones.The response is shown on a scale from 0 to 1, where 0 is no stimulus and1 is maximum stimulus. A legend showing the grayscale ranges on thisscale is provided.

The stimuli in FIG. 6A corresponds to homogeneous red light of uniformintensity across the retina. As depicted in FIG. 6C, there is a lowresponse by the M and S cones (depicted by black squares in the mosaic)and some response by the L cones (depicted as grey squares).Accordingly, the red stimulus results in a differential stimulation ofcones within the retina, particularly L cones compared to M cones.

Prior approaches to addressing the myopiagenic effect of displaysfocused on excess stimulation of L cones compared to M cones (see, e.g.,WO 2012/145672 A1). In other words, the prior approach focused onreducing the saturation of red pixels in an image. The focus on L and Mcones is also understandable, because together they make up ˜95% ofcones in the human eye. The focus on red wavelengths in particular isalso understandable for two reasons: (1) red wavelengths stimulate L andM cones at a high differential (˜4.5:1) compared to green light (˜1:1:5)or blue light (˜1:1); and (2) artificial light from screens, for examplefrom video games and animation, contains abundant red light incomparison with sources of red in the outdoor world, where it is foundsparingly. However, the present disclosure further recognizes that highspatial frequency, high contrast images can similarly result in asimilar myopiagenic response and a more comprehensive solution shouldaccount for the effect of such images. For example, if one considersonly the amount of red in an image when applying a correction, themyopiagenic effect of a red image (e.g., that has L>M) is reduced, e.g.,by introducing a green ring around the image and/or reducing saturationof the red image by decreasing the red level and/or increasing green.However, such as approach would not apply any improvement to an image onthe basis of neighboring cone contrast. Similarly, a black and whitecheckerboard would not be improvable under the prior approach, becauseeach black and each white pixel approximates an equal energy illuminant,and therefore would not be subject to an improved L/M ratio. However,such a black/white checkerboard would be subject to improvement in thepresent disclosure, because it creates high neighboring cone contrast;methods to improve such images are disclosed and described herein.Accordingly, algorithms that account for high spatial frequency effectsare disclosed which can be used either alone or in combination withalgorithms which reduce red saturation.

Algorithms for Myopia Reduction

Turning now to algorithms for reducing the myopiagenic effect ofdisplayed images, in general, the color of each pixel in each frame canbe modified based on one or more of the following parameters: (i) thecolor of the pixel in the frame itself; (ii) the location of the pixelin the frame, such as the proximity of the pixel to the edge of theframe; (iii) the color of another pixel in the frame, such as aneighboring pixel; (iv) the color of that same pixel in another frame,such as the preceding frame; and/or (v) the color of a different pixelin a different frame.

Implementations may reduce saturation of red pixels in an image, reducecontrast between adjacent pixels, or both.

Referring to FIG. 7, an initial video signal 410 is provided thatincludes image information for a series of n initial frames, f₁ ^(i), f₂^(i), . . . , f_(n) ^(i). Each frame is composed of k pixels, p₁, p₂, .. . , p_(k). Each pixel is composed of three color component values,r^(i), g^(i), and b^(i), corresponding to values for red, green, andblue, respectively.

In step 420, a relative level of stimulation of L cones, M cones, and/orS cones is determined for each pixel in each frame based on the valuesr^(i), g^(i), and b^(i). For example, this step may simply involvecomparing the value of r^(i) to the value of g^(i) and/or b^(i) for apixel. Alternatively, or additionally, XYZ tristimulus values, LMSvalues, or other ways to measure cone stimulation may be calculated fromthe RGB values.

Next, in step 430, one or more pixels are identified for colormodification based on the relative level of L, M, and/or S conestimulation by each pixel. For example, in some embodiments, red pixelsare identified by comparing the RGB values or based on a hue of eachpixel. In other embodiments, pixels are chosen because of high levels ofcolor contrast with other neighboring pixels. In still otherembodiments, pixels are chosen because of high differences in conestimulation levels among neighboring cones.

In some embodiments, pixels are identified based on the color of otherpixels in the frame. For example, groups of adjacent red pixels (e.g.,corresponding to red objects in an image) are identified formodification but lone red pixels are left unmodified. Alternatively, oradditionally, pixels may be identified for color modification based onthe color of the same pixel in other frames. For example, in someembodiments, red pixels that persist for more than one frame (e.g., forone or several seconds, or more) may be identified for colormodification, but those red pixels that exist for only one or just a fewframes (e.g., a<1 second, <0.1 seconds, or <0.01 seconds) may be leftunmodified.

In step 440, modified image data is generated based on the relativelevel of stimulation of L cones to M cones, or the level of adjacentcone contrast, and, in some cases, other factors (e.g., user preferencesand/or aesthetic factors). A variety of modification functions may beused. In general, the modification will reduce the level of redsaturation in a pixel's color and/or reduce the contrast level betweenadjacent pixels or adjacent groups of pixels.

In some embodiments, for those pixels identified for color modification,modified image data is generated by scaling r^(i), g^(i), and/or b^(i),e.g., by a corresponding scale factor α, β, γ.

In other words:

-   -   r^(m)=αr^(i),    -   g^(m)=βg^(i), and/or    -   b^(m)=γb^(i).

In general, the scale factors α, β, and/or γ for each pixel may varydepending on a variety of factors, such as, for example r^(i), g^(i),and/or b^(i) for that pixel, r^(i), g^(i), and/or b^(i) of another pixelin the same frame, r^(i), g^(i), and/or b^(i) of the same pixel in adifferent frame, r^(i), g^(i), and/or b^(i) of a different pixel in adifferent frame, and/or other factors.

For example, in some embodiments, where r^(i)>g^(i) and >b^(i) in apixel, r^(i) may be decreased for that pixel by some amount (i.e.,0<α<1) and/or g^(i) may be increased for that pixel by some fractionalamount (i.e., 1<β). b^(i) may be unchanged (i.e., γ=1), or can beincreased or decreased. In certain implementations, α and/or β arefunctions of the difference between r^(i) and g^(i). For instance, scalefactors can be established so that the larger the difference betweenr^(i) and g^(i), the more the red value in the modified signal isreduced relative to the initial signal and/or the more the green valuein the modified signal is increased. By way of example, one simplemathematical formulation for this type of scale is:

α=k _(α)(r ^(i) −g ^(i))+c _(α), and

β=k _(β)(r ^(i) −g ^(i))+c _(α).

Here, k_(α) and k_(β) are proportionality constants and c_(α) and c_(β)are constant offsets. k_(α) is negative so that a larger differencebetween r^(i) and g^(i) results in a smaller value for α. Conversely,k_(β) is positive so that β increases proportionally to the differencebetween r^(i) and g^(i). The proportionality constants and constantoffsets may be determined empirically.

Generally, in implementations where 0<α<1 and β=γ=1, red pixels in themodified image will appear darker than in the initial image. Inimplementations where α=γ=1 and 1<β, red pixels in the modified imagewill appear whiter lighter than in the initial image. In both cases, thedegree of red saturation in the red pixels will decrease as the amountof red decreases relative to green.

In yet another embodiment, matrix multipliers may be used that create alinear transformation:

$\begin{bmatrix}r_{f} \\g_{f} \\b_{f}\end{bmatrix} = {\begin{bmatrix}\alpha \\\beta \\\gamma\end{bmatrix}\begin{bmatrix}r_{i} & g_{i} & b_{i} \\r_{i} & g_{i} & b_{i} \\r_{i} & g_{i} & b_{i}\end{bmatrix}}$

In some embodiments, values for r_(f), g_(f), and b_(f) are derived fromlinear combinations of their corresponding initial values and thedifference between r and g. To illustrate an example that is not meantto bound the invention:

r _(f) =r _(i)+α(r _(i) −g _(i))

g _(f) =g _(i)+β(r _(i) −g _(i))

b _(f) =b _(i)+γ(r _(i) −g _(i))

In one embodiment, −1<α<0 and β and γ are both values between 0 and 1.More specifically, where β=γ=−α/2, the transformation results in a finalpixel that is equilluminant to the initial pixel. The condition ofequilluminance is satisfied when(r_(f)+g_(f)+b_(f))=(r_(i)+g_(i)+b_(i)).

While the modification of each component color described above isproportional to the input component color value, non-linear scaling isalso possible (e.g., involving more than one scale factor and one ormore additional higher order terms in the input component color value).

Finally, a modified video signal 440 is output, containing imageinformation for a series of n modified frames, f₁ ^(m), f₂ ^(m), . . . ,f_(n) ^(m), each containing the same number of pixels, k, as the initialframes. For at least a subset of pixels, the RGB values are modifiedfrom the input signal. The other pixels may be unchanged from the inputsignal. For example, the color of all the red pixels may be modified,while the color of the pixels that are not red are left unchanged.

As noted previously, in some embodiments, a pixel's color is modifiedbased on the color of a different pixel in the same frame. For example,the algorithm can include adjacent red pixels (e.g., corresponding redobjects in an image), and reduce r^(i)−g^(i) for those pixels by acertain amount, while leaving isolated red pixels unchanged or reducingr^(i)−g^(i) by a different (e.g., lesser) amount.

By basing a pixel's color modification on the color of a different pixelin the same frame, the effect of color modification perceived by aviewer's visual processing in the brain may be reduced, e.g., usingperceptual illusions such as the so-called watercolor effect orso-called Cornsweet effect. In the watercolor effect, a red object mayappear to be more saturated than it actually is when the edge of theobject is more saturated than the interior. The watercolor effect may beused when modifying the color of objects in a frame, particularly whenthey are bordered by pixels that have chromaticies in opposite directionin color space or much darker pixels. See, e.g.,http://www.scholarpedia.org/article/Watercolor_illusion.

Referring to FIG. 8A, the watercolor effect is illustrated for a redcircle against a black background. The initial image features a highlysaturated, uniformly red circle. The modified image, as shown, maintainsthe highly saturated red pixels (R=255, G=0, B=0) at the boundary of thecircle, but reduces red saturation towards the interior of the circle(R=177, G=57, B=55). There is a radial gradient toward the center, wherethe gradient occurs on the outer ½ to ⅓ of the circle, avoiding theappearance of an annular discontinuity of the circle color.

The Cornsweet effect is an optical illusion where the gradient within acentral line or section creates the impression that one side of theimage appears darker than it actually is in reality. This effect may beutilized to reduce the brightness of red objects that border other redobjects, for example, to allow a reduction in myopiagenic contrast whilepreserving an impression to the viewer that the image is highlysaturated.

FIG. 8B shows an example of the Cornsweet effect. Here, the left mostside of figure appears to be a brighter red than the right hand side. Inreality, both sides have the same brightness. The illusion is created bythe dark to bright gradient between the two sides when viewed from leftto right. Using the cornsweet effect it may be possible to reduce thesaturation of certain red objects adjacent less saturated red objectswith minimal change perceived by the viewer by introducing a light todark gradient between the two objects.

Implementations that use illusions like the watercolor effect andCornsweet effect may include additional image processing steps, such asidentifying red objects in an image that may be candidates for theeffect. Establishing candidacy of objects for these effects can be donebased on factors such as the size and shape of the red object,uniformity of the red color of the object, and/or the nature of thebordering color.

In some embodiments, the modification to a red pixel's color can bemodified based on the location of the pixel in a frame. For example, ifa pixel located closer to an edge of the frame may be modified, while apixel of the same color located closer to the middle of the frame isunchanged or modified to a lesser degree.

In other embodiments, the modification to a red pixel's color can bemodified based on the type of object that the pixels represent. Certainobjects may be deemed to be important to preserve in their originalcolors. One example might be a company logo or branded product where thecolors are very recognizable. Using image analysis, those objects couldbe identified by comparison to an image database, and flagged fordifferential treatment in the algorithm.

Alternatively, or additionally, the color of a pixel in one frame may bemodified based on the color of that pixel in another frame. For example,the color of colored objects that persist over a series of frames may bemodified so that the degree of saturation of the reds in the objectlessen over time. The time scale and rate of color change may besufficient so that the effect is not easily noticeable to a viewer, buteffectively reduces color saturation or overall retinal contrast.

In another example, the degree to which red pixels are modified mayincrease over time. Accordingly, the longer the viewer views the displayduring a particular viewing session, the greater the degree ofmodification of the red pixels.

In general, the algorithm may implement one or more techniques toimprove computation efficiency and avoid, for example, issues withlatency when delivering images to a display. For example, in someembodiments, only a subset of the pixels and/or frames are evaluated formodification. For example, for purposes of computational efficiency, notevery frame is evaluated (e.g., only every other frame, or fewer, isevaluated). Such sampling may improve latency of the algorithm whenexecuted in real time.

In some embodiments, not every pixel is evaluated in every frame. Forexample, only those pixels proximate to the center of the frame (e.g.,where the viewer is more likely to focus) are evaluated. Alternatively,only those pixels distant from the center of the frame, where the vieweris less likely to notice changes, are evaluated. Alternatively, oradditionally, image analysis techniques can be applied to identify whichportions of a frame are in focus (and therefore likely to be focused onby the viewer) and apply color modification only to those pixels in thefocused portions.

In some implementations, the algorithm periodically samples pixels ineach frame in order to decide whether to evaluate other pixels. Forexample, the algorithm can check the color of every 2^(nd) or fewerpixels (e.g., every 3^(rd) pixel or fewer, every 5^(th) pixel, every10^(th) pixel or fewer, every 20^(th) pixel). In the event that thisinitial sampling detects a pixel that is a candidate for modification,the algorithm can apply color modification to the identified pixel.Pixels in between the sampled areas can either be left unmodified orfurther sampled to determine if they are candidates for modification.Alternatively, they could be modified by the same linear transformationas the initially sampled pixel, or interpolated values in betweensampled pixels could be used to determine the final pixel values. Suchsampling techniques may be useful to improve speed of the algorithm, sothat it is not necessary to evaluate every pixel in every frame.

Compression techniques used for encoding images may also be used toimprove efficiency. For example, in some embodiments, chroma subsamplingmay be used. Examples of chroma subsampling include 4:2:2, 4:2:1, 4:1:1,and 4:2:0 subsampling. This subsampling may also be useful to improvespeed of the algorithm, so that it is not necessary to evaluate everypixel in every frame. Using these techniques, the resolution of colorpixels generally is reduced so that pixel rendering of color becomeseasier without being readily noticeable to viewers. Alternatively, theresolution could be kept the same as in the initial image, andin-between pixels would be derived from interpolated values or lineartransformation based upon the sampled pixels.

Input from additional hardware components can also be used to modify thecolor modification algorithm. In some embodiments, the system caninclude an eye-tracking module in order to follow which location on thedisplay a user is viewing. Subsequently, color modification is appliedto only the location on the display being viewed. Alternatively, colormodification is applied to only the locations on the display that arenot being viewed. Commerically-available eye tracking solutions may beused for this purpose. An example of a commercially-available solutionis the Tobii EyeX Controller, available from Tobii AB (Danderyd,Sweden).

In some embodiments, the algorithm modifies those portions of an imagethat are not the focus of the viewer, but leaves the portion of theimage that is focused on unchanged. In this way, the impact of themodification on the viewing experience is reduced because the modifiedpixels are in the viewer's periphery.

Such an approach may be especially useful in applications which rendertext, such as in e-readers and word processing software. Text is oftendisplayed in high-contrast black and white which, for reasons discussedpreviously, can elicit a particularly acute myopiagenic response eventhough these images typically contain no red pixels. In someembodiments, text can be rendered in high contrast only within a portionof the image (e.g., a viewing bubble) and text outside of this area canbe display with reduced contrast and/or with a blurred effect. In someembodiments, there can be a gradient between the defocused/low contrastportion of the image and the viewing bubble. In order to facilitatereading, the bubble can be moved over the text or the text can be movedthrough a stationary bubble. The speed of relative movement may beselected according to a preferred reading speed of the user (e.g., 20words per minute or more, 50 words per minute or more, 80 words perminute or more, 100 words per minute or more, 150 words per minute ormore, 200 words per minute or more, 250 words per minute or more, 300words per minute or more, 350 words per minute or more, 400 words perminute or more, 450 words per minute or more, 500 words per minute ormore, up to about 800 words per minute).

The size and shape of the viewing bubble can also vary as desired. Theviewing bubble can correspond to an angle of about 20° or less in auser's field of view (e.g., 15° or less, 10° or less, 5° or less) in thehorizontal and/or vertical viewing directions. The viewing bubble can beelliptical, round, or some other shape. In some embodiments, the usercan set the size and/or shape of the viewing bubble.

In some embodiments, the viewing bubble can track the user's finger asit traces across lines of text. Devices may utilize a touch screen forfinger tracking. Alternatively, the bubble can be moved by tracing a,stylus, mouse, or other indicator of attention.

A variety of techniques for establishing the viewer's focus can be useddepending on the implementation. For example, eye-tracking technologycan be used to follow the location on the display a user is viewing. Thealgorithm can use information from an eye-tracking camera to identifypixels for modification in real time. Those pixels away from the viewedlocation are modified while the area of focus is unmodified (or modifiedto a lesser extent). Eye-tracking may be particularly useful in mobiledevices (e.g., using the front facing camera), computer monitors (e.g.,using a video-conferencing camera), and/or with video game consoles, forexample.

Alternative Cone Stimulation Determinations and Myopia Scales

Rather than simply compare the r^(i), g^(i), and/or b^(i) values inorder to assess whether a pixel will differentially stimulate cones,including L and M cones, in the retina, in some embodiments thealgorithm calculates other quantifiable measures of cone stimulation bythe image. For example, it is possible to model how much an image willdifferentially stimulate center-surround antagonism in the human visualsystem by directly quantifying the extent of spatial and chromaticcontrast contained in the image. Relatively high center-surroundantagonism is expected to result in a high degree of differentialstimulation and therefore a larger myopia-causing effect thancenter-surround contrast that is relatively lower. For a discussion ofcenter-surround receptive fields in the visual system, see for example,“Perception Lecture Notes: Retinal Ganglion Cell” by Prof D. Heegeravailable athttp://www.cns.nyu.edu/˜david/courses/perception/lecturenotes/ganglion/ganglion.html.

In some embodiments, the algorithm measures include only L cones and Mcones. In other embodiments, the contributions of S cones are alsoincluded. In some embodiments, calculating cone stimulation firstinvolves translating RGB values for each pixel to a color space thatquantitatively links the spectral content of the pixel to thephysiologically perceived colors in human vision. One example of such acolor space is the CIE 1931 XYZ color space, discussed previously. Thiscolor space defines the XYZ tristimulus values analogously to the LMScone responses of the human eye. Thus, rather than compare r^(i) andg^(i) in order to assess which pixels require color modification,algorithms can compare X and Y (or X, Y, and Z, if desired). Forexample, in some case, color modification is applied to those pixels forwhich X>Y and Z, but not for pixels where X≦Y and/or Z.

Alternatively, or additionally, cone stimulation values in LMS colorspace can be calculated from the XYZ tristimulus values (see, e.g.,https://en.wikipedia.org/wiki/LMS_color_space). Algorithms forperforming such calculations are known (see, e.g., the xyz2lms program,available atwww.imageval.com/ISET-Manual-201506/iset/color/transforms/xyz2lms.html).With LMS values, color modification can be applied to candidate pixels,for example those whose L values are above a certain threshold and/orthose pixels for which L>M (e.g., L>M and S).

Alternatively, cone stimulation can be calculated directly using thephysical properties of light. Light intensity and wavelength from eachof R, G, and B can be measured from a device such as a television,computer, or tablet. The intensity of each wavelength that passesthrough the eye and reaches the retina can be calculated. These valuescan then be translated into stimulation of L, M, and S cones, forexample by using the Smith-Pokorny cone fundamentals (1992) or the conefundamentals as modified by Stockman and Sharpe (2000). In general,scales derived from calculations that determine cone stimulation basedon LMS values are referred to as LMS myopia scales.

While the foregoing techniques may be useful for modifying displayedimages to reduce their myopiagenic effects, these techniques are basedsolely on the image information and do not account for variationsbetween people's retina or conditions under which the images are viewed.

It is also possible to account for varying ratios of different cones aviewer's eyes and/or varying spatial distributions of cones. This isimportant because different individuals are known to have differentproportions of L cones to M cones. In addition, different populationgroups, on average, have different proportions of L cones to M cones.Caucasians, for example, have approximately 63% L cones on average,while Asians have equal numbers of L to M cones on average. Accordingly,the myopiagenic effect of a particular stimulus can differ for differentpopulation groups.

The effects of a stimulus on differing retina may be calculated based onretina models (or ‘simulated retina’), for example. Referring to FIG. 9,an exemplary algorithm 900 for determining cone stimulation levels by anRGB formatted stimulus on a simulated retina is as follows. Algorithm900 starts (901) by establishing a simulated retina (920). Generally,this involves establishing a relative number of L, M, and S cones, andestablishing their arrangement pattern. FIG. 6B shows an example of asimulated retina. Here, different numbers of L, M, and S cones arerandomly arranged with hexagonal packing (i.e., on a brickwall-patternedgrid).

Algorithm 900 receives the stimulus pattern in RGB format (910). The RGBstimulus pattern corresponds to the colors of a pixel array, asdiscussed previously. In general, the pixel array can correspond to asingle image frame or a portion of an image frame, for example.Generally, where an input video file is being analyzed, each frame willcorrespond to a separate RGB stimulus pattern. FIG. 6A shows an exampleof a stimulus pattern.

In step 930, the RGB values for each element of the stimulus pattern areconverted into a corresponding set of XYZ tristimulus values. Suchtransformations are well-known. See, e.g., “Colour Space Conversions,”by Adrian Ford (ajoec1@wmin.ac.uk <defunct>) and Alan Roberts(Alan.Roberts@rd.bbc.co.uk), Aug. 11, 1998, available athttp://www.poynton.com/PDFs/coloureq.pdf. Next, in step 940, LMS valuesare calculated from each of the XYZ tristimulus values using, e.g.,xyz2lms.

In step 950, the stimulus pattern is then mapped onto the simulatedretina. In this example, the elements of the stimulus pattern is in a1:1 correspondence with the cones of the simulated retina and themapping results in the selection of the L, M, or S value at each elementof the stimulus pattern depending on whether the cone at thecorresponding retina location is an L cone, an M cone, or an S cone,respectively.

A stimulation level at each cone is determined from the mapping (step960). In some implementations, this determination simply involvesassigning each cone the L, M, or S value based on the mapping. Incertain cases, the LMS value is scaled to fall within a particular rangeor the LMS value is weighted to increase or decrease a contribution dueto certain portions of the spectrum or other factors.

The algorithm ends (999) after outputting the cone stimulation levels.

Implementations may involve variations of algorithm 900. For example,while algorithm 900 involves a 1:1 pixel to cone mapping, higher orlower mapping ratios may be used. For example, in some instances, conestimulation can be calculated for stimuli where more than one pixel isimaged to a single cone. This may occur, for example, in high resolutiondisplays or where a display is viewed from relatively far away. In sucharrangements, the algorithm can include an additional step of averagingthe color of groups of pixels to provide a stimulus pattern having thesame resolution and grid shape as the simulated retina. The number ofpixels per cone may vary. 2 or more pixels per cone may be used (e.g., 3or more pixels per cone, 4 or more pixels/cone, 5 or more pixels percone, 6 or more pixels per cone, 7 or more pixels per cone, 8 or morepixels per cone, 9 or more pixels per cone, or 10 pixels per cone).

In some cases, the algorithm may account for fewer than one pixel beingimaged to each cone (e.g., 2 or more cones per pixel, 3 or more conesper pixel, 4 or more cones per pixel, 5 or more cones per pixel, 6 morecones per pixel, 7 or more cones per pixel, 8 or more cones per pixel, 9or more cones per pixel, up to 10 cones per pixel). This is the casewith lower resolution displays, or when displays are viewed from acloser distance. In such cases, a pixel can be assigned to more than onegrid point in a stimulus pattern having the same resolution and gridshape as the simulated retina.

Some implementations can include calculating (i.e., accounting for) thenumber of pixels per cone for a specific display and/or user. Forexample, referring to FIGS. 12A and 12B, the number of pixels per conemay be calculated from the pixel density for a display as follows.First, the typical maximum retinal resolution, θ, of 1 arc minute, isassumed, as well as a viewing distance, d, that is typically ≈2.5 timesthe display's diagonal dimension (i.e., a 60″ TV is viewed from 12.5′away, and an iPhone 6 is viewed from a foot away). The calculation canbe adjusted for other viewing distances, as desired. Accordingly,knowing a screen's size and resolution (e.g., 1,920×1,080 for a 1080p60″ TV set, 1,334×750 for the Apple iPhone 6), one can compare thenumber of pixels per square area of screen and the number of cones persquare area of screen. The ratio of these numbers gives the number ofpixels per cone (or the reciprocal). This illustrated for a 60″ 1080P TVin FIG. 12B, for which the screen area per cone equals 0.24 mm².

Apply this calculation for a 60″ 1080P TV and iPhone 6, the pixels percone are 0.49 and 0.24, respectively.

In some embodiments, the point spread function of light can be used tomap the light coming from the pixels to cones in the retina. Asunderstood by skilled artisans, the point spread function of light isdue to imperfect optics of the human eye, and effects how incident lightstrikes the retinal cone mosaic.

In some embodiments, the equal area cone fundamentals from FIG. 1 areused to calculate the relative excitation of L, M, and S cones. Otherimplementations using other representations of the cone fundamentals arepossible. These include cone fundamentals based on quanta, thosecorrected to energy terms, and those that have been normalized to peakvalues. Cone fundamentals for either a two-degree or ten-degree observercould be used, or any other observer for which cone fundamental data isavailable can be used. In addition, these calculations can be adjustedand made specific for a person's age, macular pigmentation, cone mosaiccomposition, and/or other factors.

In some embodiments, the equal energy illuminant D65 is used forconversions between RGB, XYZ, and LMS. In other embodiments, otherilluminants can be used, such as CIE-A (incandescent lamps), CIE-C, orCIE-E.

In some embodiments, the CIECAM02 matrix is used to convert between XYZvalues and LMS values. In other embodiments, other matrices are used toperform linear transformations. Any acceptable transformation matrix (ornone at all, if XYZ values are used directly) can be used in thisrespect.

By calculating a quantifiable value for LMS cone stimulation by astimulus pattern, it is possible to quantify the degree to which a givenstimulus will differentially stimulate cones, including L cones and Mcones. This quantification allows for the scoring of a stimulus (e.g., aparticular image, a video file), which in turn—by comparingscores—allows for the objective comparison of the myopiagenic effect ofdifferent media.

Referring to FIG. 10, an algorithm 1000 for scoring a digital video fileis as follows. This algorithm, or similar algorithms, may be applied toother media, such as image files. The algorithm starts (1001) byreceiving (or generating) cone stimulus values for a simulated retinastimulated by a frame of the digital video file (step 1010). The conestimulus values may be determined using algorithm 900 shown in FIG. 9,for example.

For each cone, the algorithm calculates an average x of the LMS stimulusvalues for that cone (c) and each of its neighbors (n_(i)). In suchimplementations, cone c is considered the center of a visual receptivefield and the nearest neighbors are the surround. For m-nearestneighbors, x is calculated as:

$\overset{\_}{x} = {\frac{1}{m + 1}{( {{\sum\limits_{i = 1}^{m}\; n_{i}} + c} ).}}$

In general, the number of neighbors will depend on the cone pattern inthe stimulated retina and how many neighbors are included for each cone.In one embodiment, only the nearest neighbors are considered. Forexample, in a grid pattern, a cone has eight nearest neighbors. Such apattern is illustrated in FIG. 11A. With hexagonal packing, each conehas six nearest neighbors as shown in FIG. 11B.

In steps 1030 and 1040, the difference between the neighbor stimulusvalues, n_(i), and the average, x, is calculated, and squared, anddivided by x: (n_(i)−x)²/x. This provides a measure of the relativedifference in stimulation between the cone, c, and each of its nearestneighbors. These values are summed, providing a value for the NeighborSum of Squares (NSS) for cone, c:

${NSS} = {\sum\limits_{i = 1}^{m}\; {\frac{( {n_{1} - \overset{\_}{x}} )^{2}}{\overset{\_}{x}}.}}$

This value provides a quantitative measured of the level of stimulationof cone, c, relative to its nearest neighbors. It is believed that arelatively high NSS value represents a large differential response andcorresponds to a larger myiopiagenic response from cone, c, than a lowerNSS value.

While the sum of squares is used in this case to calculate a measure ofrelative cone stimulation, other approaches are possible. For example,the sum of absolute values of the difference between n_(i) and x may beused instead. Alternatively, the relative absolute value |n_(i)−{dotover (x)}|/x or the overall range |n_(max)−n_(min)| may be used. Otheralternatives include calculate a variance of the values or a standarddeviation.

NSS values are calculated for each cone in the stimulated retina (1060)and then the NSS values can be averaged over the entire frame (1070).This process is repeated for each frame (1080) and then the NSS valuesaveraged over all frames (1090).

Finally, the frame-averaged NSS value is scaled to a desired range(e.g., a percentage) and/or the media file is scored based on theframe-averaged NSS value.

Table 1, below, provides exemplary results of such a calculation forvarying stimuli. The first column, “Frame”, lists the stimulus for eachexperiment. A 100×100 pixel array was used (“pixel count”), and a 1:1cone-to-pixel mapping assumed. The percentage of L-to-M-to-S conesvaried as indicated in columns 2-4. The results of each calculation isprovided in column 6 (“Raw Scale”). The score is quoted raw,un-normalized to any particular value.

Other center-surround models are also possible. In general, such modelscan account for a variety of factors that are believed to influencecenter-surround interactions, such as relative center and surroundcontrasts, relative phase/collinearity, width of surround, relativeorientations, spatial frequencies, and speeds, threshold vs.suprathreshold, and individual differences, which are not generallymutually exclusive. Another model for center-surround interactions, forexample, is described by J. Xing and D. J. Heeger in “Measurement andmodeling of center-surround suppression and enhancement,” in VisionResearch, Vol. 41, Issue 5 (March 2001), pp. 571-583. Here, the model isbased on a non-linear interaction of four components: local excitation,local inhibition, surround excitation, and surround inhibition.

TABLE 1 Exemplary Myopiagenic Scale Scores Pixel Raw Frame % L % S % MCount Scale Comment R = G = 100 63 5 32 100 × 100 4.123 R = 100 63 5 32100 × 100 10.08 R = 255 63 5 32 100 × 100 79.4 G = 255 63 5 32 100 × 10061.39 R = 255 48 5 48 100 × 100 97.96 Asian ratio R = 100 48 5 48 100 ×100 12.61 Asian ratio R = G = 63 5 32 100 × 100 0.217 B = 100 R = G = 635 32 100 × 100 0.12 B = 75 R = G = 63 5 32 100 × 100 1.71 B = 255 R = G= 63 5 32 100 × 100 0 B = 0 R = 255 0 5 95 100 × 100 1.3215 protanope R= 255 95 5 0 100 × 100 14.7700 deuteranope BW Checker 63 5 32 100 × 100438.04 BW Checker 48 5 48 100 × 100 444.014 BW Checker 0 5 95 100 × 100460.9 protanope BW Checker 95 5 0 100 × 100 425.4 deuteranope

In general, the myopiagenic value can be normalized to a scale orassigned some other identifier indicative of the contents myopiageniceffect. For example, the value can be presented as a value in a range(e.g., from 1 to 10), as a percentage, or by some other alphanumericidentifier (e.g., as a letter grade), color scale, or description.

Myopiagenic scales for content, such as the scale described above, maybe useful in many ways. For example, a scale allows one to rate content(e.g., movies or other video files) as to its myopiagenic effect on aviewer.

A scale also provides an objective way to measure algorithms that modifyimages, including changing colors of images. They can be used to rateefficacy of algorithms designed to increase or decrease neighboring conecontrast. They can also be used to rate efficacy of algorithms designedto increase or decrease myopiagenicity. For example, one can comparealgorithms by comparing the score of a common video file after it ismodified using a respective algorithm. In some embodiments, one cancompare the effect on myopiagenic reduction of algorithms havingdiffering computational efficiencies using the scale. For instance, onecan evaluate the tradeoff between an algorithm that modifies every framein a video file, versus one that modifies fewer frames (e.g., everyother frame, every third frame, etc) Similarly, one can evaluate thetradeoff between algorithms that evaluate every pixel versus samplingpixels within frames.

While the examples herein describe electronic images and videos, theskilled artisan will appreciate that such a scale may be useful in thenon-digital world, for example to rate the neighboring cone contrast ormyopiagenicity of printed media, including books, newspapers, boardgames, etc. Light reflected from such physical media could be measuredand retinal stimulation could be calculated in the manner set forthabove.

E-Readers and Word Processors Designed Using a Myopiagenic Scale

Quantitative myopiagenic scales may be useful in the design of productsin addition to evaluating media. For example, myopiagenic scales can beused to evaluate combinations of colors in certain types of displays andidentify those color combinations rating favorably on the myopiagenicscale.

Such color combinations are useful when displaying text, in particular,which is commonly displayed using black text on a white background atthe maximum contrast allowed by the display. However, it is believedthat the high level of contrast between the text and background produceshigh levels of contrast at a viewer's retina, which in turn leadsmyopia. Accordingly, it is believed that the myopiagenic effects ofreading may be reduced by selecting a color combination offeringrelatively low overall cone contrast. This may be useful in displayingtext in various settings, including but not limited to e-book hardware,e-book software, word processing software, and the like.

Accordingly, a myopiagenic scale, such as the one described above, maybe useful for selecting color combinations for displaying text. This canbe accomplished by evaluating, using the scale, different combinationsof colors for text and background.

By way of example, an exemplary evaluation was performed for a series ofcolor combinations modeled using a 100×100 checkerboard of candidatetext and background colors, with varying contrast edges. This patternprovides a stimulus with 50% text color and 50% background color. Otherpatterns providing different ratios between text and background colorscan be used, which may be more representative of certain fonts, spacing,and margins (for example, approximately 5% text color, approximately 10%text color, approximately 15% text color, approximately 20% text color,approximately 25% text color, approximately 30% text color,approximately 35% text color, approximately 40% text color, orapproximately 45% text color).

A simulated retina was used having a 100×100 cone pattern in linear rowand column grid, and a 1:1 ratio of pixels to cones was used.

For purposes of the example, 8-bit color was assumed. Accordingly, eachcolor was selected with values from 0-255 for each RGB. The availablecolor space was sampled using every color in steps of 50 (6³ values foreach of text and background), resulting in a total of 6⁶ or 46,656combinations in total.

Referring to FIG. 13, a three-dimensional plot shows the results of theexperiment. The vertical scale gives the unscaled myopiagenic score. Thehorizontal axes give the respective Text Color and Background Color.Note that the values on the horizontal scales are expressed inhexadecimal, where the 0-255 RGB values is converted to hex and thecolors reported as RRGGBB.

Results range from myopiagenic scores of 0 (white text on whitebackground and black text on black background) to 419.34 (black text onwhite background). Accordingly, color combinations that provide areduced myopiagenic score compared to black text on white background(e.g., light green on cyan, with a score of 155) may be selected for usewhen displaying text.

Obviously, the lowest scores (white on white, black on black) areimpractical because they provide no contrast between text and backgroundand cannot be read. However, generally, color combinations with low butnon-zero scores can be selected. In some cases, there is a tradeoff inthe readability of the text due to low color contrast between the textand background. Accordingly, additional criteria may be considered whenselecting e-reader color combinations. For example, an objective indexfor readability may be considered. Highest readability is expected tooccur when the color system can differentiate best between text andbackground colors (e.g., when L and M values are most different betweentext and background). This is different from the myopiagenic scale whichassumes that the highest myopiagenic effect occurs when adjacent coneshave highest differential stimulation. In other words, the myopiageniceffect comes from both differences between text and background (whichimproves readability but increases myopia), but also from within textand background (which does not improve readability but increasesmyopia).

By way of example, readability (R) may be scored by surveyingrespondents. Alternatively, it can be scored based on color contrastbetween text and background using the LMS system or another colorsystem. Such differences may be quantified using a formula such as thefollowing:

$R = {{\alpha_{R}( \frac{( {L_{1} - L_{2}} )^{2}}{\frac{1}{2}( {L_{1} + L_{2}} )} )} + {\beta_{R}( \frac{( {M_{1} - M_{2}} )^{2}}{\frac{1}{2}( {M_{1} + M_{2}} )} )} + {\gamma_{R}( \frac{( {S_{1} - S_{2}} )^{2}}{\frac{1}{2}( {S_{1} + S_{2}} )} )}}$

Here, L, M, and S are the values described above for which the subscript1 refers to the text color and 2 refers to the background color. α_(R),β_(R), and γ_(R) are weighting factors for weighing the relativecontributions of cone systems. These factors can be determinedempirically. In this example, equal area functions were used for L, M,and S, and values of α_(R)=0.17, β_(R)=0.84, γ_(R)=0.01 were determinedfor a population of four observers (three trichromatic females and onemale protanope), to use an example. Readability scored using this methodis referred to herein as a “Text Readability” score.

Readability can also be scored in other ways, for example the distancebetween the two colors in CIELAB space ΔE*_(ab). This measure of colordifferentiation was described by Brainard and Stockman (Vision andVision Optics, 2009, “Chapter 10: Colorimetry”):

ΔE* _(ab)=√{square root over ((ΔL*)²+(Δa*)²+(Δb*)²)}

Referring to FIGS. 14A and 14B, results of several color combinationsfrom an experiment are tabulated. In each table, columns 1, 2, and 3 arethe RGB values for the background color (each from 0-255), columns 4-6are the corresponding X, Y, Z tristimulus values, and columns 7-9 thecorresponding LMS values. Columns 10, 11, and 12 are the RGB values forthe text color (each from 0-255), columns 13-15 are the corresponding X,Y, Z tristimulus values, and columns 16-18 the corresponding LMS values.The calculated myopiagenic scale score based on a 100×100 checkerboardgrid with 50% text/50% background is given in column 19 and the %reduction in score relative to black text on white background (row 1) isgiven in column 20. An example of the color scheme is shown in column21. The next four columns (22-25) give values related to the readabilityscore. In particular, column 22 gives the values for

$( \frac{( {L_{1} - L_{2}} )^{2}}{\frac{1}{2}( {L_{1} + L_{2}} )} ),( \frac{( {M_{1} - M_{2}} )^{2}}{\frac{1}{2}( {M_{1} + M_{2}} )} ),\mspace{14mu} {{and}{\mspace{14mu} \;}( \frac{( {S_{1} - S_{2}} )^{2}}{\frac{1}{2}( {S_{1} + S_{2}} )} )},$

respectively. Column 25 gives the readability score, R, where the valuesα_(R)=0.17, β_(R)=0.84, γ_(R)=0.01 are used. Column 26 provides acomposite score that consists of the ratio readability/myopia score.

It is instructive to consider certain examples to illustrate theimportance of considering readability when identifying text/backgroundcolor combinations for text rendering. For example, consider a firstcolor combination having RGB values of (200, 150, 150) for backgroundand (100, 150, 200) for text, respectively, and a second colorcombination having RGB values of (250, 150, 100) for background and(250, 150, 150) for text, respectively. FIG. 15A shows a table in whichcolumns 1, 2, and 3 are the RGB values for the background color, columns4-6 are the corresponding X, Y, Z tristimulus values, and columns 7-9the corresponding LMS values. Columns 10, 11, and 12 are the RGB valuesfor the text color, columns 13-15 are the corresponding X, Y, Ztristimulus values, and columns 16-18 the corresponding LMS values.Column 19 shows the myopiagenic scale score and column 20 shows thepercent reduction (as a decimal) from black text on white background;column 21 shows an example of text rendered using the color combination.Columns 22-24 give the same parameters as columns 22-24 in FIG. 14, andcolumn 25 gives the readability score. Accordingly, using the scaledescribed above, the myopia scores for the first and second combinationsare similar (both ˜18). As is evident (at least anecdotally) from theexample text in column 21, the first color combination is easier to readthan the second color combination. This is borne out by their relativereadability scores, which are approximately 2.0 and 0.1, respectively.

This is further illustrated in the plots shown in FIGS. 15B and 15C,respectively, which simulate cone stimulation for a stripe of textbetween two stripes of background across three rows having 33 coneseach. FIG. 15B shows simulated cone stimulation for the first colorcombination. In general, the text and cones have different levels ofstimulation with text stimulation levels varying approximately within arange from 32 to 40. With the exception of a few peaks of highstimulation (in this example, resulting from simulated S cones), thebackground stimulation levels vary within a lower, largelynon-overlapping range approximately from 22 to 30.

FIG. 15C shows cone stimulation levels for the second color combination.Here, variance within text and background is similar to variance betweentext and background. Both text and background have larger variancecompared to the first color combination (ranging from approximately 35to 55, with the exception of a few cones having lower stimulation valuesdue to background, in this example from simulated S cones). Conestimulation of text overlaps with cone stimulation of background.

FIGS. 16A-16C illustrate the same principle for two further colorcombination examples. Referring to FIG. 16A, the first color combinationhas RGB values (150, 150, 150) for background and (150, 50, 50) fortext. The second color combination has RGB values (250, 100, 250) forbackground and (150, 150, 200) for text. Again, anecdotally, the firstcolor combination is significantly more readable than the second colorcombination. Columns 1-26 shows the same parameters as columns 1-26 inFIG. 15A.

FIG. 16B show a plot of cone stimulation for a stripe of text betweentwo stripes of background for the first color combination. The text andbackground have significantly different levels of stimulation andvariance for within the text and within the background are low comparedto variance between text and background levels.

FIG. 16C show a plot of cone stimulation for a stripe of text betweentwo stripes of background for the second color combination. Variancewithin text and background is similar to variance between text andbackground. Both text and background have larger variance compared tothe first color combination and cone stimulation of text overlaps withcone stimulation of background.

While commercially-available e-readers include modes of operation thatdisplay text in color combinations other than black and white that mayhave a reduced myopiagenic effect compared to black text on a whitebackground, it is believed that the disclosed implementations providecolor combinations offering substantially greater reductions. Forexample, the NookColor offers “color text modes” such as “Night,”“Gray,” “Butter,” “Mocha,” and “Sepia” in addition to “Day” (basic blacktext against white background (see, e.g.,http://www.dummies.com/how-to/content/nook-tablet-text-and-brightness-tools.html).In particular, “Night” is described as “white type against a black orgray background.” “Gray” is “black text on a light gray background.”“Butter” uses “dark brown text against a pale yellow page.” “Mocha” is“white text against a light brown backdrop,” and “sepia” is “black textagainst a yellow-brown page.” It is instructive to calculate myopiascores for these modes based on estimated RGB values (8-bit)corresponding to these descriptions using the LMS myopia model describedabove. These estimates and corresponding scores, along with readabilityvalues, R, are summarized in table 2, below.

TABLE 2 Myopia and Readability Scores for Estimated NookColor ModesBackground Text Reduction Mode R G B R G B Score % Readability CompositeNight 0 0 0 255 255 255 438 0 208 0.48 Gray 150 150 150 0 0 0 133 70 640.48 Butter 255 255 224 165 42 42 300 32 171 0.57 Mocha 200 100 100 255255 255 200 54 119 0.60 Sepia 175 175 21 0 0 0 190 57 88 0.47

Accordingly, it is believed that such modes offer a lowest myopia scoreof about 133 (as calculated using the scale described above which yieldsa score of about 438 for black (0, 0, 0) text on white (255, 255, 255)background) and a readability/myopia score ratio in a range from about0.47 to 0.60.

As is evident from the tables shown in FIGS. 14A and 14B, colorcombinations having a myopia score using the LMS myopia scale of lessthan about 130 are possible (e.g., about 120 or less, about 110 or less,about 100 or less, about 90 or less, about 80 or less, about 70 or less,about 60 or less, about 50 or less, about 40 or less, about 30 or less,such as from about 20 to about 30). Compared to black and white text,such colors can offer an improvement in myopia reduction of about 65% ormore (e.g., about 70% or more, about 75% or more, about 80% or more,about 85% or more, about 90% or more, about 95% or more). Colorcombinations having a composite readability/myopia score of 0.80 or moreare possible (e.g., 0.85 or more, 0.90 or more, 0.95 or more, 1.00 ormore, 1.05 or more, 1.10 or more, 1.15 or more, 1.20 or more, 1.25 ormore, 1.30 or more, 1.35 or more, 1.40 or more, such as 1.45).

In general, e-reader or word processing solutions based on the above maybe implemented in a variety of ways. For example, in an e-reader with acolor display or an e-reader application on a mobile device, colorcombinations with favorable myopiagenic scores and readability scoresmay be selected by the user as an option. For example, during setup orvia a settings menu, the e-reader can present the user with a variety ofcolor combination options, from which the user can selected a desirablechoice. This is advantageous because preferred color combinations areexpected to vary from user to user and providing a selection of choiceswill allow each user to use a color combination most desirable to them.By analogy, word processing solutions could be determined in a similarfashion.

Monochrome e-readers, on the other hand, such as those usingelectrophoretic displays, may be used having color combinations havereduced myopiagenic scores and relatively good readability based onscales such as the those described above. In some implementations ofmonochrome e-readers, each pixel is composed of one or more“microcapsules” containing two types of pigmented particles havingopposite charge. When a charge is applied to a particular pixel, theparticles having like charge are repelled from one side of the pixel tothe other, and those having opposite charge are attracted. Accordingly,by reversing the charge on the pixel, the pixel can take on the color ofone pigment or the other, or various combinations of the two dependingon how long the charge is applied. According, in embodiments, pigmentscan be selected (alone or in combination with black and/or whitepigments) to correspond to color combinations that have reducedmyopiagenic scores relative to black and white pigments. When displayed,such pigment combinations can reduce contrast between adjacent neuronsof the retina and/or reduce center-surround antagonism.

In some embodiments, a user can input a desired level of myopiareduction and the e-reader returns a selection of color combinationsthat correspond to the desired level. For example, FIG. 17 shows analgorithm 1700 in which a user can select text-background colorcombinations having a desired level of myopia reduction. Here, as partof the e-reader setup or within a menu of options that are part of thee-reader's operating system, for example, the e-reader presents the userwith an interface, such as an input box, slider, dropdown box, radiobuttons, or other input tool, in which the user can input a desiredlevel of myopia reduction. The desired level can be a minimum amount ofmyopia reduction, a range of myopia reduction values, or a single valueindicative of the desired level. Levels may be expressed as a percentage(e.g., where the most myopiagenic combination corresponds to 0%reduction and the most myopia reducing combination is 100%) or on someother scale (e.g., from 0 to 10 or some other alphanumeric scale).

Upon receiving the user's input (step 1710), algorithm 1700 retrievescolor combinations corresponding to the level designated by the user andpresents one or more combinations to the user (step 1720). The colorcombinations can be calculated using a myopia scale such as by thealgorithm, or can be calculated beforehand and stored in a database(e.g., locally or remote) that is accessed by the algorithm.

The number of color combinations presented to the user can vary. Forexample, the algorithm can present only a subset of combinations thatmost closely match the user's desired level (e.g., 10 or fewer, 8 orfewer, 5 or fewer). In some implementations, the algorithm can presentthose color combinations that match the user's desired myopia reductionlevel within a certain range (e.g., within 10% of the desired level,within 5%, within 2%, within 1%).

Upon viewing the presented color combinations, the user selects thedesired combination. Upon receiving the selection (step 1730), thealgorithm displays text using the selected color combination (step1740).

In some embodiments, the algorithm can present color combinations to theuser based on one or more criteria in addition to the desired level ofmyopia reduction. For instance, the user can be presented colorcombinations based on a readability score (see above) in addition tolevel of myopia reduction. Alternatively, the user can be presentedcolor combinations based on the preferences gathered from other users orthe preferences previously expressed by a particular user and/or derivedby previous behavior of a particular user or group of users.

In some embodiments, the algorithm includes a recommendation engine thatprovides a selection of myopia-reducing color combinations based on thenature of content in the e-book. For instance, the recommendation canvary depending on whether the e-book is primarily text (e.g., a novel ornonfiction book), contains both text and figures (e.g., a textbook,magazine, or newspaper), or is primarily figures (e.g., a graphic novelor comic). Recommended color combinations for different e-book contentcan be based on a myopiagenic scale (e.g., the LMS scale describedabove) which is used to evaluate the myopiagenic effect of differenttypes of content. Alternatively, or additionally, recommendations can bebased on data collected and observed about user preferences (e.g., theindividual user in front of the screen at the moment, broad sets of userdata about which is accumulated over time from many users, or both) thatmay be preferable or suitable for e-reading different types of content.

In certain implementations, an e-reader can include modes for users: aconventional mode that displays e-books using conventional colorschemes, and a myopia-safe mode for displaying e-books using a colorcombination with a reduced myopiagenic effect compared to theconventional mode. In other words, different color combinations can beassociated with different accounts on device. For example, an e-readercan feature a user experience that allows a parent to create settingsfor children (e.g., one or more) as well as themselves that havedifferent myopia reduction levels. In other words, kids may not be ableto select color combinations when operating the e-reader under theiraccount (or at least have a reduced ability to change display colors).Accordingly, in certain implementations, an administrator (e.g., adultaccount) can associate color combinations with a myopia-reduced modewhich will then be used by the e-reader when e-books are accessed usingcertain user accounts (e.g., children's accounts).

Moreover, in certain embodiments, the color combinations used to presenttext and background can vary (automatically, or upon prompting) overtime. For instance, in some embodiments, a myopia-reduced mode can begina reading session using a color combination have a first level of myopiareduction and change the color combination as the reading sessionprogresses. For example, colors with increasing myopia reduction can beused as a reading session progresses (e.g., as measured by time orprogress in reading the content). The color changes can happenautomatically. Alternatively, the user can be prompted to change thecolor combination as the reading session progresses. In someembodiments, the e-reader can change between color combinations thathave similar myopia scores as a reading session progresses, e.g., simplyto present a change for the user. Myopia-reduced color combinations canbe implemented in an e-reader in a variety of ways. For example,myopia-reduced color combinations can be included as part of theoperating system of the e-reader as discussed above. Alternatively, themyopia-reduced color combinations can be implemented via software as anadd-on to existing e-reader programs or as standalone e-readerapplications that can be installed on an e-reader, other mobile device,or any other device used for reading e-books.

In general, any format e-book can be displayed using a combination ofcolors that have a reduced myopia potential compared to black and white,including (without limitation) Broadband eBooks (BBeB) (e.g., e-bookfiles using extensions .lrf; .lrx), Comic Book Archive file (e.g.,e-book files using file extensions .cbr (RAR); .cbz (ZIP); .cb7 (7z);.cbt (TAR); .cba (ACE)), Compiled HTML (e.g., e-book files usingextension .chm), DAISY-ANSI/NISO Z39.86, DjVu (e.g., e-book files usingextension .djvu), DOC (e.g., e-book files using extension .DOC), DOCX(e.g., e-book files using extension .DOCX), EPUB (e.g., e-book filesusing extension .epub), eReader (e.g., e-book files using extension.pdb), FictionBook (e.g., e-book files using extension .fb2), APABI(e.g., e-book files using extensions .xeb; .ceb), Hypertext MarkupLanguage (e.g., e-book files using extensions .htm; .html and typicallyauxiliary images, js and css), iBook (e.g., e-book files using extension.ibooks), IEC 62448, INF (e.g., e-book files using extension .inf), KF8(Amazon Kindle) (e.g., e-book files using extensions .azw3; .azw; .kf8),Microsoft LIT (e.g., e-book files using extension .lit), MOBI orMobipocket (e.g., e-book files using extensions .prc; .mobi), MultimediaeBooks (e.g., e-book files using extensions .exe or .html), Newton eBook(e.g., e-book files using extension .pkg), Open Electronic Package(e.g., e-book files using extension .opf), Portable Document Format(e.g., e-book files using extension .pdf), Plain text files (e.g.,e-book files using extension .txt), Plucker (e.g., e-book files usingextension .pdb), PostScript (e.g., e-book files using extension .ps),Rich Text Format (e.g., e-book files using extension .rtf), SSReader(e.g., e-book files using extension .pdg), Text Encoding Initiative(e.g., e-book files using extension .xml), TomeRaider (e.g., e-bookfiles using extensions .tr2; .tr3), and Open XML Paper Specification(e.g., e-book files using extensions .oxps, .xps).

Aspects of the systems and methods described here can be implemented indigital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.For example, in some implementations, the electronic processing modulesdisclosed above can be implemented using digital electronic circuitry,or in computer software, firmware, or hardware, or in combinations ofone or more of them.

The term “electronic processing module” encompasses all kinds ofapparatus, devices, and machines for processing data and/or controlsignal generation, including by way of example a programmable processor,a computer, a system on a chip, or multiple ones, or combinations, ofthe foregoing. The module can include special purpose logic circuitry,e.g., an FPGA (field programmable gate array) or an ASIC (applicationspecific integrated circuit). The module can also include, in additionto hardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, across-platform runtime environment, a virtual machine, or a combinationof one or more of them. The module and execution environment can realizevarious different computing model infrastructures, such as web services,distributed computing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages. A computer program may, but need not, correspondto a file in a file system. A program can be stored in a portion of afile that holds other programs or data (e.g., one or more scripts storedin a markup language document), in a single file dedicated to theprogram in question, or in multiple coordinated files (e.g., files thatstore one or more modules, sub programs, or portions of code). Acomputer program can be deployed to be executed on one computer or onmultiple computers that are located at one site or distributed acrossmultiple sites and interconnected by a communication network.

Some of the processes described above can be performed by one or moreprogrammable processors executing one or more computer programs toperform actions by operating on input data and generating output. Theprocesses and logic flows can also be performed by, and apparatus canalso be implemented as, special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application specificintegrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andprocessors of any kind of digital computer. Generally, a processor willreceive instructions and data from a read only memory or a random accessmemory or both. A computer includes a processor for performing actionsin accordance with instructions and one or more memory devices forstoring instructions and data. A computer may also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Devices suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices (e.g., EPROM, EEPROM, flash memory devices, and others),magnetic disks (e.g., internal hard disks, removable disks, and others),magneto optical disks, and CD ROM and DVD-ROM disks. The processor andthe memory can be supplemented by, or incorporated in, special purposelogic circuitry.

To provide for interaction with a user, operations can be implemented ona computer having a display device (e.g., a flat panel display, oranother type of display device) for displaying information to the userand a keyboard and a pointing device (e.g., a mouse, a trackball, atablet, a touch sensitive screen, or another type of pointing device) bywhich the user can provide input to the computer. Other kinds of devicescan be used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

A computing system may include a single computing device, or multiplecomputers that operate in proximity or generally remote from each otherand typically interact through a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), an inter-network (e.g., the Internet), a networkcomprising a satellite link, and peer-to-peer networks (e.g., ad hocpeer-to-peer networks). A relationship of client and server may arise byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

FIG. 18 shows an example electronic processing module 800 that includesa processor 810, a memory 820, a storage device 830 and an input/outputdevice 840. Each of the components 810, 820, 830 and 840 can beinterconnected, for example, by a system bus 850. The processor 810 iscapable of processing instructions for execution within the system 800.In some implementations, the processor 810 is a single-threadedprocessor, a multi-threaded processor, or another type of processor. Theprocessor 810 is capable of processing instructions stored in the memory820 or on the storage device 830. The memory 820 and the storage device830 can store information within the module 800.

The input/output device 840 provides input/output operations for themodule 800. In some implementations, the input/output device 840 caninclude one or more of a network interface devices, e.g., an Ethernetcard, a serial communication device, e.g., an RS-232 port, and/or awireless interface device, e.g., an 802.11 card, a 3G wireless modem, a4G wireless modem, etc. In some implementations, the input/output devicecan include driver devices configured to receive input data and sendoutput data to other input/output devices, e.g., keyboard, printer anddisplay devices 860. In some implementations, mobile computing devices,mobile communication devices such as smart phones or tablet computers,and other devices can be used.

Other embodiments are in the following claims.

What is claimed is:
 1. A method for displaying an e-book using acombination of colors for text and background that have a reducedmyopiagenic effect compared to black text on white background, themethod comprising: presenting a user with one or more combinations ofcolors for the text and background identified as having a reducedmyopiagenic effect, wherein none of the presented combinations compriseeither black or white text or either black or white background, and,when viewed by the user's retina, an image composed of text andbackground rendered in any of the presented color combinations providesreduced center-surround contrast on the user's retina compared to theimage viewed as black text on white background; receiving a selection ofone of the color combinations from the user; and displaying the e-bookfile using the combination of colors for the text and backgroundselected by the user.
 2. The method of claim 1, wherein the reducedcenter-surround contrast due to the color combinations yields amyopiagenic effect reduced by at least 35% as calculated using a myopiascale that calculates a center-surround contrast of a modeled visualreceptive field and assigns a score to the color combinations based onthe calculated center-surround contrast.
 3. The method of claim 2,wherein the reduced center-surround contrast due to the colorcombinations yields a myopiagenic effect reduced by at least 40% ascalculated using the myopia scale.
 4. The method of claim 2, wherein thereduced differential stimulation due to the color combinations yields amyopiagenic effect reduced by at least 50% as calculated using themyopia scale.
 5. The method of claim 2, wherein the reduced differentialstimulation due to the color combinations yields a myopiagenic effectreduced by at least 60% as calculated using the myopia scale.
 6. Themethod of claim 2, wherein the center-surround contrast is calculatedbased on a difference between an average stimulation of the visualreceptive field center versus its and a stimulation of the surround. 7.The method of claim 6, wherein the visual receptive field centercorresponds to a cone and the surround to its nearest neighbors.
 8. Themethod of claim 7, wherein the average stimulation is determined basedon LMS stimulus values of the cone and its nearest neighbors of thevisual receptive field.
 9. The method of claim 1, further comprisingreceiving information about a desired myopiagenic level from the userand presenting the one or more combinations of colors according to thereceived information, the presented combinations of colors having amyopiagenic effect corresponding to the desired level.
 10. The method ofclaim 9, wherein the information about the desired myopiagenic level isa desired percentage reduction of myopia potential as calculated using amyopia scale that calculates an impact on the retina based on adifferential stimulation between the center and surround of a modeledvisual receptive field.
 11. The method of claim 10, wherein thepresented combinations of colors have a myopiagenic level within 10% ofthe desired percentage reduction of myopia potential as calculated usingthe myopia scale.
 12. The method of claim 10, wherein the presentedcombinations of colors have a myopiagenic level within 5% of the desiredpercentage reduction of myopia potential as calculated using the myopiascale.
 13. The method of claim 10, wherein the presented combinations ofcolors have a myopiagenic level within 2% of the desired percentagereduction of myopia potential as calculated using the myopia scale. 14.The method of claim 10, wherein the myopia scale is a LMS Myopia Scale.15. The method of claim 1, wherein the e-book is a file in a formatselected from the group consisting of: Broadband eBooks (BBeB), ComicBook Archive, Compiled HTML, DAISY, DjVu, DOC, DOCX, EPUB, eReader,FictionBook, Founder Electronics, HTML, iBook, IEC62448, INF, KF8, KPF,Microsoft LIT, MOBI, Mobipocket, Multimedia eBooks, Newton eBook, OpenElectronic Package, PDF, Plain text, Plucker, PostScript, RTF, SSReader,Text Encoding Initiative, TomeRaider, and Open XML Paper Specification.16. The method of claim 1, wherein the e-book is displayed on a mobiledevice.
 17. The method of claim 16, wherein the mobile device is asmailphone, a tablet computer, or a dedicated e-reader.
 18. A device fordisplaying an e-book, comprising: a display; an interface for receivinginput from a user; and an electronic processing module programmed tocause the device to: present the user with one or more combinations ofcolors for text and background identified as having a reducedmyopiagenic effect, wherein none of the presented combinations compriseeither black or white text or either black or white background, and,when viewed by the user's retina, an image composed of text andbackground rendered in any of the presented color combinations providesreduced center-surround contrast on the user's retina compared to theimage viewed as black text on white background; receive a selection ofone of the color combinations from the user via the interface; retrievethe e-book from memory; and display, using the display, the e-book usingthe combination of colors for the text and background selected by theuser.
 19. The device of claim 18, wherein the reduced center-surroundcontrast due to the color combinations yields a myopiagenic effectreduced by at least 35% as calculated using a myopia scale thatcalculates a center-surround contrast of a modeled visual receptivefield and assigns a score to the color combinations based on thecalculated center-surround contrast.
 20. The device of claim 19, whereinthe reduced center-surround contrast due to the color combinationsyields a myopiagenic effect reduced by at least 60% as calculated usingthe myopia scale.
 21. The device of claim 20, wherein thecenter-surround contrast is calculated based on a difference between anaverage stimulation of the visual receptive field and a stimulation ofthe surround.
 22. The device of claim 19, wherein the visual receptivefield corresponds to a cone and its nearest neighbors.
 23. The device ofclaim 18, wherein the electronic processing module is further programmedto cause the device to receive information about a desired myopiageniclevel from the user and present the one or more combinations of colorsaccording to the received information, the presented combinations ofcolors having a myopiagenic effect corresponding to the desired level.24. The device of claim 23, wherein the information about the desiredmyopiagenic level is a desired percentage reduction of myopia potentialas calculated using a myopia scale that calculates an impact on theretina based on a differential stimulation between the center andsurround of a modeled visual receptive field.
 25. The device of claim18, wherein the interface comprises a touch panel.
 26. The device ofclaim 18, wherein the display is a flat panel display.
 27. The device ofclaim 18, wherein the device is a smartphone, a tablet computer, or adedicated e-reader.
 28. A method for displaying an e-book using acombination of colors for text and background that have a reducedmyopiagenic effect compared to black text on white background, themethod comprising: displaying text using a text color other than blackor white; and displaying a background to the text using a backgroundcolor other than black or white; wherein an image displayed using thedisplayed text color on the displayed background color, when viewed bythe user's retina, provides reduced center-surround contrast on theuser's retina compared to the image when viewed in black and white. 29.The method of claim 28, wherein the text color and background coloryield a ratio of a Text Readability score to myopia score on a LMSmyopia scale is greater than 0.60.
 30. The method of claim 28, whereinthe myopia potential is reduced by more than 58% as calculated using aLMS myopia scale and a Text Readability score is decreased no more than65% compared to the image when viewed as black text on white background.